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alefiury/wav2vec2-large-xlsr-53-coraa-brazilian-portuguese-gain-normalization-sna
alefiury
2022-04-05T16:59:13Z
7
2
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "pt", "portuguese-speech-corpus", "PyTorch", "dataset:CORAA", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:voxforge", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-26T18:58:07Z
--- language: pt datasets: - CORAA - common_voice - mls - cetuc - voxforge metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch license: apache-2.0 model-index: - name: Alef Iury XLSR Wav2Vec2 Large 53 Portuguese results: - task: name: Speech Recognition type: automatic-speech-recognition metrics: - name: Test CORAA WER type: wer value: 24.89% --- # Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets: - [CORAA dataset](https://github.com/nilc-nlp/CORAA) - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz). - [Multilingual Librispeech (MLS)](http://www.openslr.org/94/). - [VoxForge](http://www.voxforge.org/). - [Common Voice 6.1](https://commonvoice.mozilla.org/pt). ## Repository The repository that implements the model to be trained and tested is avaible [here](https://github.com/alefiury/SE-R_2022_Challenge_Wav2vec2).
alefiury/wav2vec2-large-xlsr-53-coraa-brazilian-portuguese-gain-normalization
alefiury
2022-04-05T16:58:36Z
9
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "pt", "portuguese-speech-corpus", "PyTorch", "dataset:CORAA", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:voxforge", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-27T16:34:54Z
--- language: pt datasets: - CORAA - common_voice - mls - cetuc - voxforge metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch license: apache-2.0 model-index: - name: Alef Iury XLSR Wav2Vec2 Large 53 Portuguese results: - task: name: Speech Recognition type: automatic-speech-recognition metrics: - name: Test CORAA WER type: wer value: 24.89% --- # Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets: - [CORAA dataset](https://github.com/nilc-nlp/CORAA) - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz). - [Multilingual Librispeech (MLS)](http://www.openslr.org/94/). - [VoxForge](http://www.voxforge.org/). - [Common Voice 6.1](https://commonvoice.mozilla.org/pt). ## Repository The repository that implements the model to be trained and tested is avaible [here](https://github.com/alefiury/SE-R_2022_Challenge_Wav2vec2).
facebook/wav2vec2-large-960h-lv60
facebook
2022-04-05T16:42:07Z
7,394
6
transformers
[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "speech", "en", "dataset:librispeech_asr", "arxiv:2006.11477", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 model-index: - name: wav2vec2-large-960h-lv60 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Librispeech (clean) type: librispeech_asr args: en metrics: - name: Test WER type: wer value: 2.2 --- # Wav2Vec2-Large-960h-Lv60 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").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 **facebook/wav2vec2-large-960h-lv60** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60").to("cuda") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60") def map_to_pred(batch): inputs = processor(batch["audio"]["array"], return_tensors="pt", padding="longest") input_values = inputs.input_values.to("cuda") attention_mask = inputs.attention_mask.to("cuda") with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).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=16, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | |---|---| | 2.2 | 4.5 |
jeremykke/bert-base-uncased-finetuned-swag
jeremykke
2022-04-05T15:29:55Z
2
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "dataset:swag", "license:apache-2.0", "endpoints_compatible", "region:us" ]
multiple-choice
2022-04-05T03:34:21Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - swag metrics: - accuracy model-index: - name: bert-base-uncased-finetuned-swag 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. --> # bert-base-uncased-finetuned-swag This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 1.0087 - Accuracy: 0.7911 ## 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: 16 - 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 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7545 | 1.0 | 4597 | 0.5963 | 0.7695 | | 0.3914 | 2.0 | 9194 | 0.6152 | 0.7879 | | 0.1385 | 3.0 | 13791 | 1.0087 | 0.7911 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
HenryHXR/scibert_scivocab_uncased-finetuned-ner
HenryHXR
2022-04-05T15:24:38Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-04-05T14:23:14Z
--- tags: - generated_from_trainer model-index: - name: scibert_scivocab_uncased-finetuned-ner 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. --> # scibert_scivocab_uncased-finetuned-ner This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. ## 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: 5 ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
BigSalmon/InformalToFormalLincolnConciseWordy
BigSalmon
2022-04-05T15:21:32Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-05T15:17:33Z
``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy") ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. *** wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. *** sweet: ``` Keywords to sentences or sentence.
gaetangate/bart-large_genrl_simpleq
gaetangate
2022-04-05T15:09:05Z
6
0
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "arxiv:2108.07337", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- license: apache-2.0 --- This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking) ## Citation ```bibtex @inproceedings{rossiello-genrl-2021, title={Generative relation linking for question answering over knowledge bases}, author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan}, booktitle={International Semantic Web Conference}, pages={321--337}, year={2021}, organization={Springer}, url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19", doi = "10.1007/978-3-030-88361-4_19" } ```
tbosse/bert-base-german-cased-finetuned-subj_v3
tbosse
2022-04-05T15:03:58Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-04-05T13:32:50Z
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-german-cased-finetuned-subj_v3 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. --> # bert-base-german-cased-finetuned-subj_v3 This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1790 - Precision: 0.1875 - Recall: 0.0079 - F1: 0.0152 - Accuracy: 0.9472 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 136 | 0.1721 | 0.0 | 0.0 | 0.0 | 0.9488 | | No log | 2.0 | 272 | 0.1731 | 0.0 | 0.0 | 0.0 | 0.9482 | | No log | 3.0 | 408 | 0.1790 | 0.1875 | 0.0079 | 0.0152 | 0.9472 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
chibubu/Deeplearning_for_vision
chibubu
2022-04-05T14:36:36Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2022-04-05T14:29:18Z
--- license: apache-2.0 ---
onecat1/1
onecat1
2022-04-05T14:30:12Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2022-04-05T14:30:12Z
--- license: apache-2.0 ---
medhabi/distilbert-base-uncased-mlm-ta-local
medhabi
2022-04-05T14:05:55Z
2
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-04-05T11:20:38Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-mlm-ta-local 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. --> # distilbert-base-uncased-mlm-ta-local This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0658 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4431 | 1.0 | 3125 | 2.1817 | | 2.2197 | 2.0 | 6250 | 2.0929 | | 2.1519 | 3.0 | 9375 | 2.0696 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.1 - Datasets 2.0.0 - Tokenizers 0.11.6
naver-clova-ocr/bros-base-uncased
naver-clova-ocr
2022-04-05T13:56:46Z
40,502
18
transformers
[ "transformers", "pytorch", "bros", "feature-extraction", "arxiv:2108.04539", "endpoints_compatible", "region:us" ]
feature-extraction
2022-03-02T23:29:05Z
# BROS GitHub: https://github.com/clovaai/bros ## Introduction BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents.<br> Given the OCR results of the document image, which are text and bounding box pairs, it can perform various key information extraction tasks, such as extracting an ordered item list from receipts.<br> For more details, please refer to our paper: BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents<br> Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park<br> AAAI 2022 - Main Technical Track [[arXiv]](https://arxiv.org/abs/2108.04539) ## Pre-trained models | name | # params | Hugging Face - Models | |---------------------|---------:|-------------------------------------------------------------------------------------------------| | bros-base-uncased (**this**) | < 110M | [naver-clova-ocr/bros-base-uncased](https://huggingface.co/naver-clova-ocr/bros-base-uncased) | | bros-large-uncased | < 340M | [naver-clova-ocr/bros-large-uncased](https://huggingface.co/naver-clova-ocr/bros-large-uncased) |
Kuray107/librispeech-100h-supervised-aug
Kuray107
2022-04-05T12:57:44Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-31T03:24:38Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: librispeech-100h-supervised-aug 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. --> # librispeech-100h-supervised-aug This model is a fine-tuned version of [Kuray107/librispeech-5h-supervised](https://huggingface.co/Kuray107/librispeech-5h-supervised) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0776 - Wer: 0.0327 ## 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: 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: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3099 | 1.12 | 1000 | 0.0748 | 0.0521 | | 0.1873 | 2.24 | 2000 | 0.0674 | 0.0440 | | 0.146 | 3.36 | 3000 | 0.0671 | 0.0406 | | 0.1233 | 4.48 | 4000 | 0.0619 | 0.0381 | | 0.1098 | 5.61 | 5000 | 0.0618 | 0.0381 | | 0.0985 | 6.73 | 6000 | 0.0590 | 0.0355 | | 0.0907 | 7.85 | 7000 | 0.0659 | 0.0352 | | 0.0837 | 8.97 | 8000 | 0.0679 | 0.0359 | | 0.0762 | 10.09 | 9000 | 0.0701 | 0.0349 | | 0.0707 | 11.21 | 10000 | 0.0715 | 0.0348 | | 0.0666 | 12.33 | 11000 | 0.0719 | 0.0346 | | 0.0631 | 13.45 | 12000 | 0.0746 | 0.0347 | | 0.0593 | 14.57 | 13000 | 0.0757 | 0.0340 | | 0.0554 | 15.7 | 14000 | 0.0746 | 0.0337 | | 0.053 | 16.82 | 15000 | 0.0757 | 0.0331 | | 0.0525 | 17.94 | 16000 | 0.0752 | 0.0327 | | 0.0514 | 19.06 | 17000 | 0.0776 | 0.0327 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
robvanderg/bert-base-multilingual-cased-segment1
robvanderg
2022-04-05T12:39:54Z
7
0
transformers
[ "transformers", "pytorch", "bert", "feature-extraction", "hack", "multilingual", "dataset:Wikipedia", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-04-05T12:27:21Z
--- language: - multilingual tags: - hack datasets: - Wikipedia --- ## bert-base-multilingual-cased-segment1 This is a version of multilingual bert (bert-base-multilingual-cased), where the segment embedding of the 1's is copied into the 0's. Yes, that's all there is to it. We have found that this improves performance substantially in low-resource setups for word-level tasks (e.g. average 2.5 LAS on a variety of UD treebanks). More details are to be released in our LREC2022 paper titled: Frustratingly Easy Performance Improvements for Cross-lingual Transfer: A Tale on BERT and Segment Embeddings. These embeddings are generated by the following code ``` import AutoModel baseEmbeddings = AutoModel.from_pretrained("bert-base-multilingual-cased") tte = baseEmbeddings.embeddings.token_type_embeddings.weight.clone().detach() baseEmbeddings.embeddings.token_type_embeddings.weight[0,:] = tte[1,:] ``` More details and other varieties can be found in the repo: https://bitbucket.org/robvanderg/segmentembeds/ Note that when using this model on a single sentence task (or word-level task), the results would be similar as just using `token_type_id=1` for all tokens.
johnowhitaker/colorb_gan
johnowhitaker
2022-04-05T07:43:07Z
0
1
null
[ "pytorch", "region:us" ]
null
2022-04-05T06:55:12Z
A lightweightgan trained briefly on https://huggingface.co/datasets/johnowhitaker/colorbs See https://huggingface.co/johnowhitaker/orbgan_e1 for training script and so on, since this was basically just copying that and running on a new dataset. Note: lightweightgan code was updated between training orbgan_e1 and this one, so if you're trying to run the CPU inference notebook you'll get errors. See an updated version running this model on a CPU here: https://colab.research.google.com/drive/16XKJ7XZeSI0rvUf1GU6m9qrmwr1pMRWy?usp=sharing See demo on spaces here: https://huggingface.co/spaces/huggan/Colorb_GAN
emon1521/wav2vec2-base-timit-demo-colab
emon1521
2022-04-05T07:32:44Z
2
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-04-04T04:21:00Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab 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. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## 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: 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: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
johnowhitaker/orbgan_dark
johnowhitaker
2022-04-05T07:31:24Z
0
0
null
[ "pytorch", "region:us" ]
null
2022-04-03T14:54:33Z
A version of https://huggingface.co/johnowhitaker/orbgan_e1 trained on only dark images
agi-css/distilbert-base-uncased-finetuned-truthful
agi-css
2022-04-05T07:23:56Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-05T07:09:11Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-truthful 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. --> # distilbert-base-uncased-finetuned-truthful This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4660 - Accuracy: 0.87 - F1: 0.8697 ## 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: 9.910294163459086e-05 - train_batch_size: 400 - eval_batch_size: 400 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 5 | 0.6509 | 0.59 | 0.5780 | | No log | 2.0 | 10 | 0.4950 | 0.77 | 0.7701 | | No log | 3.0 | 15 | 0.4787 | 0.81 | 0.8099 | | No log | 4.0 | 20 | 0.4936 | 0.81 | 0.8096 | | No log | 5.0 | 25 | 0.4443 | 0.82 | 0.82 | | No log | 6.0 | 30 | 0.4547 | 0.85 | 0.8497 | | No log | 7.0 | 35 | 0.4268 | 0.85 | 0.8500 | | No log | 8.0 | 40 | 0.4790 | 0.87 | 0.8697 | | No log | 9.0 | 45 | 0.4660 | 0.87 | 0.8697 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1 - Datasets 2.0.0 - Tokenizers 0.11.0
UWB-AIR/MQDD-pretrained
UWB-AIR
2022-04-05T06:14:47Z
8
0
transformers
[ "transformers", "pytorch", "longformer", "feature-extraction", "arxiv:2203.14093", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]
feature-extraction
2022-03-25T16:16:40Z
--- license: cc-by-nc-sa-4.0 --- # MQDD - Multimodal Question Duplicity Detection This repository publishes pre-trained model for the paper [MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper. The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our [Stack Overflow Dataset repository](https://github.com/kiv-air/StackOverflowDataset). To acquire the fine-tuned model, see [UWB-AIR/MQDD-duplicate](https://huggingface.co/UWB-AIR/MQDD-duplicates). The MQDD model, which is based on a Longformer architecture and is pre-trained on 218.5M training examples. The model was trained using MLM training objective accompanied with our novel Same Post (SP) and Question Answer (QA) learning objectives targeting specifically the duplicate detection task. The model can be loaded using the following source code snippet: ```Python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-pretrained") model = AutoModel.from_pretrained("UWB-AIR/MQDD-pretrained") ``` ## Licence This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/ ## How should I cite the MQDD? For now, please cite [the Arxiv paper](https://arxiv.org/abs/2203.14093): ``` @misc{https://doi.org/10.48550/arxiv.2203.14093, doi = {10.48550/ARXIV.2203.14093}, url = {https://arxiv.org/abs/2203.14093}, author = {Pašek, Jan and Sido, Jakub and Konopík, Miloslav and Pražák, Ondřej}, title = {MQDD -- Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} } ```
avialfont/dummy-translation-marian-kde4-en-to-fr
avialfont
2022-04-05T04:27:40Z
3
0
transformers
[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-04-04T19:57:33Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: avialfont/dummy-translation-marian-kde4-en-to-fr 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. --> # avialfont/dummy-translation-marian-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9807 - Validation Loss: 0.8658 - Epoch: 0 ## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.9807 | 0.8658 | 0 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.6
gagan3012/fake-news-fatima-fellowship
gagan3012
2022-04-05T04:04:39Z
16
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-04T21:45:24Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: fake-news-fatima-fellowship 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. --> # fake-news-fatima-fellowship This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 - F1: 1.0 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.008 | 1.0 | 2514 | 0.0011 | 0.9996 | 0.9996 | | 0.0004 | 2.0 | 5028 | 0.0000 | 1.0 | 1.0 | | 0.0003 | 3.0 | 7542 | 0.0000 | 1.0 | 1.0 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6
agi-css/distilbert-base-uncased-finetuned-moral-action
agi-css
2022-04-05T03:21:19Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-04T23:52:54Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-moral-action 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. --> # distilbert-base-uncased-finetuned-moral-action This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4632 - Accuracy: 0.7912 - F1: 0.7912 ## 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: 9.716387809233253e-05 - train_batch_size: 2000 - eval_batch_size: 2000 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 10 | 0.5406 | 0.742 | 0.7399 | | No log | 2.0 | 20 | 0.4810 | 0.7628 | 0.7616 | | No log | 3.0 | 30 | 0.4649 | 0.786 | 0.7856 | | No log | 4.0 | 40 | 0.4600 | 0.7916 | 0.7916 | | No log | 5.0 | 50 | 0.4632 | 0.7912 | 0.7912 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1 - Datasets 2.0.0 - Tokenizers 0.11.0
GleamEyeBeast/ascend_with_timit
GleamEyeBeast
2022-04-05T03:08:14Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-04-04T15:47:08Z
--- tags: - generated_from_trainer model-index: - name: ascend_with_timit 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. --> # ascend_with_timit This model is a fine-tuned version of [GleamEyeBeast/ascend_with_timit](https://huggingface.co/GleamEyeBeast/ascend_with_timit) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8013 - Wer: 0.4781 - Cer: 0.1727 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 2.4026 | 1.0 | 890 | 1.3419 | 0.9083 | 0.3670 | | 1.1926 | 2.0 | 1780 | 0.9730 | 0.6491 | 0.2585 | | 0.9104 | 3.0 | 2670 | 0.8483 | 0.5368 | 0.1963 | | 0.7718 | 4.0 | 3560 | 0.8122 | 0.4913 | 0.1791 | | 0.7013 | 5.0 | 4450 | 0.8013 | 0.4781 | 0.1727 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
agi-css/distilbert-base-uncased-finetuned-moral-ctx-action-conseq
agi-css
2022-04-05T02:48:15Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-05T01:58:12Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-moral-ctx-action-conseq 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. --> # distilbert-base-uncased-finetuned-moral-ctx-action-conseq This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1111 - Accuracy: 0.9676 - F1: 0.9676 ## 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: 9.989502318502869e-05 - train_batch_size: 2000 - eval_batch_size: 2000 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 10 | 0.1569 | 0.9472 | 0.9472 | | No log | 2.0 | 20 | 0.1171 | 0.9636 | 0.9636 | | No log | 3.0 | 30 | 0.1164 | 0.9664 | 0.9664 | | No log | 4.0 | 40 | 0.1117 | 0.9672 | 0.9672 | | No log | 5.0 | 50 | 0.1111 | 0.9676 | 0.9676 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1 - Datasets 2.0.0 - Tokenizers 0.11.0
huggingtweets/zei_squirrel
huggingtweets
2022-04-05T00:41:35Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-05T00:39:55Z
--- language: en thumbnail: http://www.huggingtweets.com/zei_squirrel/1649119290934/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/951980805542350848/Xx1LczLK_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">☀️👀</div> <div style="text-align: center; font-size: 14px;">@zei_squirrel</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ☀️👀. | Data | ☀️👀 | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 96 | | Short tweets | 276 | | Tweets kept | 2877 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wdkqqknq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zei_squirrel's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rrz7w9d) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rrz7w9d/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/zei_squirrel') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/hasanthehun
huggingtweets
2022-04-05T00:22:36Z
3
1
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1207601173756174336/djTLQauA_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">hasanabi</div> <div style="text-align: center; font-size: 14px;">@hasanthehun</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from hasanabi. | Data | hasanabi | | --- | --- | | Tweets downloaded | 3231 | | Retweets | 619 | | Short tweets | 202 | | Tweets kept | 2410 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6atkn60d/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @hasanthehun's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2a6l3ych) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2a6l3ych/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/hasanthehun') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
BigSalmon/MediumInformalToFormalLincoln
BigSalmon
2022-04-04T22:25:35Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-04T21:54:23Z
``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln") model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. *** - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. *** - ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. *** Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. *** Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. *** Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. *** Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. *** Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about *** Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers *** Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of *** Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone *** Search: What are phrase synonyms for " ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. *** original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. *** wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. *** sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. *** 1: ``` Keywords to sentences or sentence.
wanyu/IteraTeR-ROBERTA-Intention-Classifier
wanyu
2022-04-04T20:13:42Z
10
5
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "dataset:IteraTeR_full_sent", "arxiv:2203.03802", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-13T19:10:06Z
--- datasets: - IteraTeR_full_sent --- # IteraTeR RoBERTa model This model was obtained by fine-tuning [roberta-large](https://huggingface.co/roberta-large) on [IteraTeR-human-sent](https://huggingface.co/datasets/wanyu/IteraTeR_human_sent) dataset. Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br> Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang ## Edit Intention Prediction Task Given a pair of original sentence and revised sentence, our model can predict the edit intention for this revision pair.<br> More specifically, the model will predict the probability of the following edit intentions: <table> <tr> <th>Edit Intention</th> <th>Definition</th> <th>Example</th> </tr> <tr> <td>clarity</td> <td>Make the text more formal, concise, readable and understandable.</td> <td> Original: It's like a house which anyone can enter in it. <br> Revised: It's like a house which anyone can enter. </td> </tr> <tr> <td>fluency</td> <td>Fix grammatical errors in the text.</td> <td> Original: In the same year he became the Fellow of the Royal Society. <br> Revised: In the same year, he became the Fellow of the Royal Society. </td> </tr> <tr> <td>coherence</td> <td>Make the text more cohesive, logically linked and consistent as a whole.</td> <td> Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br> Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. </td> </tr> <tr> <td>style</td> <td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td> <td> Original: She was last seen on 2005-10-22. <br> Revised: She was last seen on October 22, 2005. </td> </tr> <tr> <td>meaning-changed</td> <td>Update or add new information to the text.</td> <td> Original: This method improves the model accuracy from 64% to 78%. <br> Revised: This method improves the model accuracy from 64% to 83%. </td> </tr> </table> ## Usage ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier") model = AutoModelForSequenceClassification.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier") id2label = {0: "clarity", 1: "fluency", 2: "coherence", 3: "style", 4: "meaning-changed"} before_text = 'I likes coffee.' after_text = 'I like coffee.' model_input = tokenizer(before_text, after_text, return_tensors='pt') model_output = model(**model_input) softmax_scores = torch.softmax(model_output.logits, dim=-1) pred_id = torch.argmax(softmax_scores) pred_label = id2label[pred_id.int()] ```
Sevil/t5-small-finetuned-wikihow_3epoch_v2
Sevil
2022-04-04T20:03:46Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wikihow", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-04-04T13:45:46Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikihow metrics: - rouge model-index: - name: t5-small-finetuned-wikihow_3epoch_v2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wikihow type: wikihow args: all metrics: - name: Rouge1 type: rouge value: 27.48 --- <!-- 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-wikihow_3epoch_v2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.2758 - Rouge1: 27.48 - Rouge2: 10.7621 - Rougel: 23.4136 - Rougelsum: 26.7923 - Gen Len: 18.5424 ## 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.8423 | 0.13 | 5000 | 2.5715 | 25.2685 | 8.6964 | 21.229 | 24.5773 | 18.4479 | | 2.7345 | 0.25 | 10000 | 2.5236 | 24.982 | 8.7823 | 21.1609 | 24.3066 | 18.3631 | | 2.6811 | 0.38 | 15000 | 2.4911 | 25.7585 | 9.3372 | 21.8388 | 25.1052 | 18.3997 | | 2.6611 | 0.51 | 20000 | 2.4510 | 26.022 | 9.4708 | 22.0899 | 25.3236 | 18.5472 | | 2.6133 | 0.64 | 25000 | 2.4272 | 26.3481 | 9.6769 | 22.4484 | 25.7046 | 18.3863 | | 2.6083 | 0.76 | 30000 | 2.4108 | 26.4131 | 9.6643 | 22.4021 | 25.6958 | 18.5585 | | 2.5842 | 0.89 | 35000 | 2.3866 | 26.2852 | 9.7505 | 22.4525 | 25.5908 | 18.5485 | | 2.5554 | 1.02 | 40000 | 2.3816 | 26.3018 | 9.7218 | 22.3673 | 25.6515 | 18.4912 | | 2.4895 | 1.14 | 45000 | 2.3730 | 26.6439 | 9.9665 | 22.6593 | 25.9521 | 18.5635 | | 2.4781 | 1.27 | 50000 | 2.3541 | 26.8488 | 10.0364 | 22.8202 | 26.1598 | 18.4254 | | 2.4821 | 1.4 | 55000 | 2.3440 | 26.9511 | 10.2079 | 23.0133 | 26.2821 | 18.5712 | | 2.4593 | 1.53 | 60000 | 2.3370 | 26.945 | 10.3123 | 22.9245 | 26.2493 | 18.5978 | | 2.4521 | 1.65 | 65000 | 2.3309 | 26.9652 | 10.314 | 22.9657 | 26.298 | 18.4837 | | 2.4523 | 1.78 | 70000 | 2.3249 | 27.0548 | 10.4204 | 23.1286 | 26.379 | 18.4717 | | 2.4563 | 1.91 | 75000 | 2.3079 | 27.4563 | 10.6452 | 23.3985 | 26.7812 | 18.5642 | | 2.4229 | 2.03 | 80000 | 2.3115 | 27.0538 | 10.44 | 22.9957 | 26.349 | 18.5914 | | 2.3694 | 2.16 | 85000 | 2.3017 | 27.332 | 10.6556 | 23.3135 | 26.629 | 18.459 | | 2.3749 | 2.29 | 90000 | 2.2941 | 27.3294 | 10.5967 | 23.2039 | 26.6411 | 18.5179 | | 2.3779 | 2.42 | 95000 | 2.2891 | 27.3725 | 10.6539 | 23.3455 | 26.707 | 18.5367 | | 2.3638 | 2.54 | 100000 | 2.2895 | 27.3487 | 10.6738 | 23.2894 | 26.681 | 18.6128 | | 2.3549 | 2.67 | 105000 | 2.2833 | 27.408 | 10.6903 | 23.3575 | 26.7137 | 18.6035 | | 2.3652 | 2.8 | 110000 | 2.2788 | 27.561 | 10.8202 | 23.4672 | 26.8584 | 18.5565 | | 2.3553 | 2.93 | 115000 | 2.2758 | 27.48 | 10.7621 | 23.4136 | 26.7923 | 18.5424 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
aswinsson/fake_new_classifier
aswinsson
2022-04-04T18:50:02Z
7
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-04T18:35:15Z
--- license: afl-3.0 --- The fake news classifer built using distillbert uncased. Created for the Fatima Fellowship coding challenge and trained on P100 instance for 3 epochs. The model is a binary classifier which predicts 1 in case of real news. Library: transformers \ Language: English \ Dataset: https:\/\/www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
mgreenbe/607-demo-model
mgreenbe
2022-04-04T17:35:06Z
6
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "tag2", "en", "dataset:yelp_polarity", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-04T17:07:37Z
--- language: - en tags: - text-classification - tag2 license: apache-2.0 datasets: - yelp_polarity metrics: - accuracy --- Demo model for predicting the polarity of Yelp reviews. Trained for 1 epoch on 4096 reviews.
efederici/cross-encoder-bert-base-stsb
efederici
2022-04-04T17:09:02Z
4
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "cross-encoder", "sentence-similarity", "it", "dataset:stsb_multi_mt", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-04T16:26:27Z
--- pipeline_tag: text-classification language: - it datasets: - stsb_multi_mt tags: - cross-encoder - sentence-similarity - transformers --- # Cross-Encoder This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. <p align="center"> <img src="https://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" width="400"> </br> Edouard Vuillard, Sunlit Interior </p> ## Training Data This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance ```python from sentence_transformers import CrossEncoder model = CrossEncoder('efederici/cross-encoder-umberto-stsb') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
efederici/cross-encoder-umberto-stsb
efederici
2022-04-04T16:09:44Z
5
0
transformers
[ "transformers", "pytorch", "camembert", "text-classification", "cross-encoder", "sentence-similarity", "it", "dataset:stsb_multi_mt", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-04T15:48:58Z
--- pipeline_tag: text-classification language: - it datasets: - stsb_multi_mt tags: - cross-encoder - sentence-similarity - transformers --- # Cross-Encoder This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. <p align="center"> <img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" width="700"> </br> Marco Lodola, Monument to Umberto Eco, Alessandria 2019 </p> ## Training Data This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance ```python from sentence_transformers import CrossEncoder model = CrossEncoder('efederici/cross-encoder-umberto-stsb') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
frahman/bert-base-uncased-issues-128
frahman
2022-04-04T15:11:09Z
5
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-04-04T13:00:28Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-issues-128 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. --> # bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2551 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0984 | 1.0 | 291 | 1.7081 | | 1.6512 | 2.0 | 582 | 1.4289 | | 1.4854 | 3.0 | 873 | 1.3845 | | 1.3924 | 4.0 | 1164 | 1.3844 | | 1.3375 | 5.0 | 1455 | 1.1944 | | 1.2969 | 6.0 | 1746 | 1.2848 | | 1.2443 | 7.0 | 2037 | 1.2678 | | 1.1998 | 8.0 | 2328 | 1.2151 | | 1.1805 | 9.0 | 2619 | 1.1638 | | 1.1396 | 10.0 | 2910 | 1.2131 | | 1.1333 | 11.0 | 3201 | 1.1966 | | 1.0974 | 12.0 | 3492 | 1.1687 | | 1.0822 | 13.0 | 3783 | 1.2283 | | 1.0736 | 14.0 | 4074 | 1.1640 | | 1.0595 | 15.0 | 4365 | 1.1207 | | 1.0515 | 16.0 | 4656 | 1.2551 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
medhabi/bert-base-uncased-finetuned-imdb
medhabi
2022-04-04T14:29:57Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-04-04T12:47:31Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-finetuned-imdb 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. --> # bert-base-uncased-finetuned-imdb This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2887 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6449 | 1.0 | 157 | 2.3557 | | 2.4402 | 2.0 | 314 | 2.2897 | | 2.3804 | 3.0 | 471 | 2.3011 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Yaxin/ernie_2.0_skep_large_en
Yaxin
2022-04-04T14:23:29Z
3
0
transformers
[ "transformers", "pytorch", "bert", "en", "arxiv:2005.05635", "endpoints_compatible", "region:us" ]
null
2022-04-04T13:45:07Z
--- language: en --- # SKEP- ## Introduction SKEP (SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis) is proposed by Baidu in 2020, SKEP propose Sentiment Knowledge Enhanced Pre-training for sentiment analysis. Sentiment masking and three sentiment pre-training objectives are designed to incorporate various types of knowledge for pre-training model. More detail: https://aclanthology.org/2020.acl-main.374.pdf ## ⚠️ attention Compared with the full version of the ernie_2.0_skep_large_en, we lost the task_embeddings part in order to adapt to the Bert framework. ## Released Model Info |Model Name|Language|Model Structure| |:---:|:---:|:---:| |skep-ernie2-bert-large| English |Layer:24, Hidden:1024, Heads:24| This released pytorch model is converted from the officially released PaddlePaddle SKEP model and a series of experiments have been conducted to check the accuracy of the conversion. - Official PaddlePaddle SKEP repo: 1. https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/skep 2. https://github.com/baidu/Senta - Pytorch Conversion repo: Not released yet ## How to use ```Python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Yaxin/ernie_2.0_skep_large_en") model = AutoModel.from_pretrained("Yaxin/ernie_2.0_skep_large_en") ``` ## Citation ```bibtex @article{tian2020skep, title={SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis}, author={Tian, Hao and Gao, Can and Xiao, Xinyan and Liu, Hao and He, Bolei and Wu, Hua and Wang, Haifeng and Wu, Feng}, journal={arXiv preprint arXiv:2005.05635}, year={2020} } ``` ``` reference: https://github.com/nghuyong/ERNIE-Pytorch ```
enimai/OPUS-mt-en-fr-finetuned-MUST-C
enimai
2022-04-04T11:49:17Z
8
0
transformers
[ "transformers", "pytorch", "marian", "text2text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-04-04T11:26:27Z
--- license: apache-2.0 ---
ramazan/fatima-cats
ramazan
2022-04-04T11:30:22Z
0
0
null
[ "cifar", "cats", "upsidedown", "dataset:cifar10_reduced", "license:mit", "model-index", "region:us" ]
null
2022-04-04T09:05:27Z
--- language: - Python - PyTorch tags: - cifar - cats - upsidedown license: mit datasets: - cifar10_reduced metrics: - Accuracy - Precision - Recall model-index: - name: CatsNet results: - task: type: image-classification name: Image Classification dataset: type: cifar10 name: CIFAR10_CATS metrics: - type: Accuracy value: 0.83 name: Test Accuracy - type: Precision value: 0.83 name: Test Precision - type: Recall value: 0.82 name: Test Recall --- Model for Fatima Fellowship code challenge. <br> Full training and evaluation pipelines can be found at: https://colab.research.google.com/drive/1hjHn6EggRDsxOZz5fMo6ZdT-4aKcUCTt
kleinay/qanom-seq2seq-model-baseline
kleinay
2022-04-04T11:05:47Z
24
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "semantic-role-labeling", "question-answer generation", "en", "dataset:kleinay/qanom", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- language: - en tags: - semantic-role-labeling - question-answer generation - pytorch datasets: - kleinay/qanom --- # A Seq2Seq model for QANom parsing This is a `t5-small` pretrained model, fine-tuned on the task of generating QANom QAs. "QANom" stands for "QASRL for Nominalizations", which is an adaptation of [QASRL (Question-Answer driven Semantic Role Labeling)](https://qasrl.org) for the nominal predicates domain. See the [QANom paper](https://aclanthology.org/2020.coling-main.274/) for details about the task. The QANom Dataset official site is a [Google drive](https://drive.google.com/drive/folders/15PHKVdPm65ysgdkV47z6J_73kETk7_of), but we also wrapped it into a [Huggingface Dataset](https://huggingface.co/datasets/biu-nlp/qanom), which is easier to plug-and-play with (check out our [HF profile](https://huggingface.co/biu-nlp) for other related datasets, such as QASRL, QAMR, QADiscourse, and QA-Align). ## Demo Visit [our demo](https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo) for interactively exploring our model! ## Usage The model and tokenizer can be downloaded as simply as running: ```python import transformers model = transformers.AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-baseline") tokenizer = transformers.AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-baseline") ``` However, the model fine-tuning procedure involves input preprocessing (marking the predicate in the sentence, T5's "task prefix", incorporating the predicate type and/or the verbal for of the nominalization) and output postprocessing (parsing the sequence into a list of QASRL-formatted QAs). In order to use the model for QANom parsing easily, we suggest downloading the [`pipeline.py`](https://huggingface.co/kleinay/qanom-seq2seq-model-baseline/blob/main/pipeline.py) file from this repository, and then use the `QASRL_Pipeline` class: ```python from pipeline import QASRL_Pipeline pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline") pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal") ``` Which will output: ```json [{'generated_text': 'who _ _ researched something _ _ ?<extra_id_7> Luke', 'QAs': [{'question': 'who researched something ?', 'answers': ['Luke']}]}] ``` You can learn more about using `transformers.pipelines` in the [official docs](https://huggingface.co/docs/transformers/main_classes/pipelines). Notice that you need to specify which word in the sentence is the predicate, about which the question will interrogate. By default, you should precede the predicate with the `<predicate>` symbol, but you can also specify your own predicate marker: ```python pipe("The student was interested in Luke 's <PRED> research about see animals .", verb_form="research", predicate_type="nominal", predicate_marker="<PRED>") ``` In addition, you can specify additional kwargs for controling the model's decoding algorithm: ```python pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal", num_beams=3) ```
blacktree/distilbert-base-uncased-finetuned-sst2
blacktree
2022-04-04T10:44:22Z
15
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-01T12:29:26Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.5091743119266054 --- <!-- 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. --> # distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7027 - Accuracy: 0.5092 ## 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: 0.01 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6868 | 1.0 | 1053 | 0.7027 | 0.5092 | | 0.6868 | 2.0 | 2106 | 0.7027 | 0.5092 | | 0.6867 | 3.0 | 3159 | 0.6970 | 0.5092 | | 0.687 | 4.0 | 4212 | 0.6992 | 0.5092 | | 0.6866 | 5.0 | 5265 | 0.6983 | 0.5092 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
somosnlp-hackathon-2022/readability-es-sentences
somosnlp-hackathon-2022
2022-04-04T10:41:09Z
21
5
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "spanish", "bertin", "es", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-30T12:30:08Z
--- language: es license: cc-by-4.0 tags: - spanish - roberta - bertin pipeline_tag: text-classification widget: - text: La ciencia nos enseña, en efecto, a someter nuestra razón a la verdad y a conocer y juzgar las cosas como son, es decir, como ellas mismas eligen ser y no como quisiéramos que fueran. --- # Readability ES Sentences for two classes Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts. ## Description and performance This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary classification among the following classes: - Simple. - Complex. It achieves a F1 macro average score of 0.8923, measured on the validation set. ## Model variants - `readability-es-sentences` (this model). Two classes, sentence-based dataset. - [`readability-es-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-paragraphs). Two classes, paragraph-based dataset. - [`readability-es-3class-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-3class-sentences). Three classes, sentence-based dataset. - [`readability-es-3class-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-3class-paragraphs). Three classes, paragraph-based dataset. ## Datasets - [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of: * coh-metrix-esp corpus. * Various text resources scraped from websites. - Other non-public datasets: newsela-es, simplext. ## Training details Please, refer to [this training run](https://wandb.ai/readability-es/readability-es/runs/3rgvwps0/overview) for full details on hyperparameters and training regime. ## Biases and Limitations - Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set. - One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases. - Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes. - Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented. - No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#bias-examples-spanish). ## Authors - [Laura Vásquez-Rodríguez](https://lmvasque.github.io/) - [Pedro Cuenca](https://twitter.com/pcuenq) - [Sergio Morales](https://www.fireblend.com/) - [Fernando Alva-Manchego](https://feralvam.github.io/)
nherve/flaubert-oral-ft
nherve
2022-04-04T10:27:14Z
3
1
transformers
[ "transformers", "pytorch", "bert", "language-model", "flaubert", "french", "flaubert-base", "uncased", "asr", "speech", "oral", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding", "fr", "license:mit", "endpoints_compatible", "region:us" ]
null
2022-03-23T12:33:05Z
--- language: fr license: mit tags: - bert - language-model - flaubert - french - flaubert-base - uncased - asr - speech - oral - natural language understanding - NLU - spoken language understanding - SLU - understanding --- # FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling **FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaubert) using the same parameters as the [flaubert-base-uncased](https://huggingface.co/flaubert/flaubert_base_uncased) model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased). ## Available FlauBERT-Oral models - `flaubert-oral-asr` : trained from scratch on ASR data, keeping the BPE tokenizer and vocabulary of flaubert-base-uncased - `flaubert-oral-asr_nb` : trained from scratch on ASR data, BPE tokenizer is also trained on the same corpus - `flaubert-oral-mixed` : trained from scratch on a mixed corpus of ASR and text data, BPE tokenizer is also trained on the same corpus - `flaubert-oral-ft` : fine-tuning of flaubert-base-uncased for a few epochs on ASR data ## Usage for sequence classification ```python flaubert_tokenizer = FlaubertTokenizer.from_pretrained("nherve/flaubert-oral-asr") flaubert_classif = FlaubertForSequenceClassification.from_pretrained("nherve/flaubert-oral-asr", num_labels=14) flaubert_classif.sequence_summary.summary_type = 'mean' # Then, train your model ``` ## References If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers: ``` @InProceedings{herve2022flaubertoral, author = {Herv\'{e}, Nicolas and Pelloin, Valentin and Favre, Benoit and Dary, Franck and Laurent, Antoine and Meignier, Sylvain and Besacier, Laurent}, title = {Using ASR-Generated Text for Spoken Language Modeling}, booktitle = {Proceedings of "Challenges & Perspectives in Creating Large Language Models" ACL 2022 Workshop}, month = {May}, year = {2022} } ```
nherve/flaubert-oral-mixed
nherve
2022-04-04T10:26:49Z
4
0
transformers
[ "transformers", "pytorch", "flaubert", "bert", "language-model", "french", "flaubert-base", "uncased", "asr", "speech", "oral", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding", "fr", "license:mit", "endpoints_compatible", "region:us" ]
null
2022-03-18T13:46:50Z
--- language: fr license: mit tags: - bert - language-model - flaubert - french - flaubert-base - uncased - asr - speech - oral - natural language understanding - NLU - spoken language understanding - SLU - understanding --- # FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling **FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaubert) using the same parameters as the [flaubert-base-uncased](https://huggingface.co/flaubert/flaubert_base_uncased) model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased). ## Available FlauBERT-Oral models - `flaubert-oral-asr` : trained from scratch on ASR data, keeping the BPE tokenizer and vocabulary of flaubert-base-uncased - `flaubert-oral-asr_nb` : trained from scratch on ASR data, BPE tokenizer is also trained on the same corpus - `flaubert-oral-mixed` : trained from scratch on a mixed corpus of ASR and text data, BPE tokenizer is also trained on the same corpus - `flaubert-oral-ft` : fine-tuning of flaubert-base-uncased for a few epochs on ASR data ## Usage for sequence classification ```python flaubert_tokenizer = FlaubertTokenizer.from_pretrained("nherve/flaubert-oral-asr") flaubert_classif = FlaubertForSequenceClassification.from_pretrained("nherve/flaubert-oral-asr", num_labels=14) flaubert_classif.sequence_summary.summary_type = 'mean' # Then, train your model ``` ## References If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers: ``` @InProceedings{herve2022flaubertoral, author = {Herv\'{e}, Nicolas and Pelloin, Valentin and Favre, Benoit and Dary, Franck and Laurent, Antoine and Meignier, Sylvain and Besacier, Laurent}, title = {Using ASR-Generated Text for Spoken Language Modeling}, booktitle = {Proceedings of "Challenges & Perspectives in Creating Large Language Models" ACL 2022 Workshop}, month = {May}, year = {2022} } ```
tanlq/vit-base-patch16-224-in21k-finetuned-cifar10
tanlq
2022-04-04T08:20:16Z
76
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:cifar10", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-03-31T03:09:09Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - cifar10 metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-finetuned-cifar10 results: - task: name: Image Classification type: image-classification dataset: name: cifar10 type: cifar10 args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9875 --- <!-- 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. --> # vit-base-patch16-224-in21k-finetuned-cifar10 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0503 - Accuracy: 0.9875 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3118 | 1.0 | 1562 | 0.1135 | 0.9778 | | 0.2717 | 2.0 | 3124 | 0.0619 | 0.9867 | | 0.1964 | 3.0 | 4686 | 0.0503 | 0.9875 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6
ydshieh/bert-base-cased-squad2
ydshieh
2022-04-04T08:06:26Z
93
0
transformers
[ "transformers", "tf", "bert", "question-answering", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]
question-answering
2022-04-01T15:23:10Z
--- license: cc-by-4.0 --- This is a BERT base cased model trained on SQuAD v2
DMetaSoul/sbert-chinese-dtm-domain-v1
DMetaSoul
2022-04-04T07:25:03Z
17
5
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-25T10:18:38Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - semantic-search - chinese --- # DMetaSoul/sbert-chinese-dtm-domain-v1 此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型,在 OPPO 手机助手小布对话匹配数据集([BUSTM](https://github.com/xiaobu-coai/BUSTM))上进行训练调优,适用于**开放领域的对话匹配**场景(偏口语化),比如: - 哪有好玩的 VS. 这附近有什么好玩的地方 - 定时25分钟 VS. 计时半个小时 - 我要听王琦的歌 VS. 放一首王琦的歌 注:此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-dtm-domain-v1-distill),也已经开源啦! # Usage ## 1. Sentence-Transformers 通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型,首先进行安装: ``` pip install -U sentence-transformers ``` 然后使用下面的代码来载入该模型并进行文本表征向量的提取: ```python from sentence_transformers import SentenceTransformer sentences = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"] model = SentenceTransformer('DMetaSoul/sbert-chinese-dtm-domain-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## 2. HuggingFace Transformers 如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话,也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取: ```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 = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-dtm-domain-v1') model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-dtm-domain-v1') # 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, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation 该模型在公开的几个语义匹配数据集上进行了评测,计算了向量相似度跟真实标签之间的相关性系数: | | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | | ------------------------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | | **sbert-chinese-dtm-domain-v1** | 78.36% | 74.46% | 32.18% | 75.95% | 44.01% | 14.50% | 66.85% | ## Citing & Authors E-mail: [email protected]
DMetaSoul/sbert-chinese-qmc-domain-v1
DMetaSoul
2022-04-04T07:24:17Z
10
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-25T09:06:52Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - semantic-search - chinese --- # DMetaSoul/sbert-chinese-qmc-domain-v1 此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型,在百度知道问题匹配数据集([LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html))上进行训练调优,适用于**开放领域的问题匹配**场景,比如: - 洗澡用什么香皂好?vs. 洗澡用什么香皂好 - 大连哪里拍婚纱照好点? vs. 大连哪里拍婚纱照比较好 - 银行卡怎样挂失?vs. 银行卡丢了怎么挂失啊? 注:此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-domain-v1-distill),也已经开源啦! # Usage ## 1. Sentence-Transformers 通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型,首先进行安装: ``` pip install -U sentence-transformers ``` 然后使用下面的代码来载入该模型并进行文本表征向量的提取: ```python from sentence_transformers import SentenceTransformer sentences = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"] model = SentenceTransformer('DMetaSoul/sbert-chinese-qmc-domain-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## 2. HuggingFace Transformers 如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话,也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取: ```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 = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1') model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1') # 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, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation 该模型在公开的几个语义匹配数据集上进行了评测,计算了向量相似度跟真实标签之间的相关性系数: | | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | | ------------------------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | | **sbert-chinese-qmc-domain-v1** | 80.90% | 76.63% | 34.51% | 77.06% | 52.96% | 12.98% | 59.48% | ## Citing & Authors E-mail: [email protected]
BigSalmon/GPT2Neo1.3BPoints
BigSalmon
2022-04-04T05:14:11Z
5
0
transformers
[ "transformers", "pytorch", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2022-04-04T04:17:46Z
``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPT2Neo1.3BPoints") model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. *** - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. *** - ```
foongminwong/dl-nlp
foongminwong
2022-04-04T05:09:39Z
0
0
null
[ "region:us" ]
null
2022-04-04T02:54:33Z
## Coding Challenge - Deep Learning for NLP (Foong) ### Description: This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news. Dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset Libraries used: Scikit-learn, NLTK, pandas, numpy, csv ### Write-up: The accuracy of the model is 0.995. There are a couple of misclassified news articles and to improve the model's performance on these news articles, here're some suggestions: - Remove stop words: The news article title and text contain a lot of commonly used words which should be removed as features. Therefore, more data cleaning should be performed prior to model building. - Try using the neural network by setting batch size, apply dropout & finetuning it - Run cross-validation
somosnlp-hackathon-2022/electricidad-base-generator-fake-news
somosnlp-hackathon-2022
2022-04-04T04:04:01Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "electra", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-29T19:52:54Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: electricidad-base-generator-fake-news 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. --> # electricidad-base-generator-fake-news This model is a fine-tuned version of [mrm8488/electricidad-base-generator](https://huggingface.co/mrm8488/electricidad-base-generator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0067 - Accuracy: 1.0 ## 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: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1136 | 1.0 | 180 | 0.0852 | 1.0 | | 0.0267 | 2.0 | 360 | 0.0219 | 1.0 | | 0.0132 | 3.0 | 540 | 0.0108 | 1.0 | | 0.0091 | 4.0 | 720 | 0.0075 | 1.0 | | 0.0077 | 5.0 | 900 | 0.0067 | 1.0 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
somosnlp-hackathon-2022/unam_tesis_ROBERTA_GOB_finnetuning
somosnlp-hackathon-2022
2022-04-04T02:19:15Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2022-04-04T01:57:47Z
--- license: apache-2.0 --- # Unam_tesis_ROBERTA_GOB_finnetuning: Unam's thesis classification with PlanTL-GOB-ES/roberta-large-bne This model is created from the finetuning of the pre-model for RoBERTa large trained with data from the National Library of Spain (BNE) [ PlanTL-GOB-ES] (https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne), using PyTorch framework, and trained with a set of theses of the National Autonomous University of Mexico [UNAM](https://tesiunam.dgb.unam.mx/F?func=find-b-0&local_base=TES01). The model classifies for five (Psicología, Derecho, Química Farmacéutico Biológica, Actuaría, Economía) possible careers at the University of Mexico. List of careers from a text. ## Training Dataset 1000 documents (Thesis introduction, Author´s first name, Author´s last name, Thesis title, Year, Career ) | Careers | Size | |--------------|----------------------| | Actuaría | 200 | | Derecho| 200 | | Economía| 200 | | Psicología| 200 | | Química Farmacéutico Biológica| 200 | ## Example of use For further details on how to use unam_tesis_ROBERTA_GOB_finnetuning you can visit the Huggingface Transformers library, starting with the Quickstart section. Unam_tesis models can be accessed simply as 'hackathon-pln-e/unam_tesis_beto_finnetuning' by using the Transformers library. An example of how to download and use the models on this page can be found in this colab notebook. ```python tokenizer = AutoTokenizer.from_pretrained('PlanTL-GOB-ES/roberta-large-bne', use_fast=False) model = AutoModelForSequenceClassification.from_pretrained( 'hackathon-pln-e/unam_tesis_ROBERTA_GOB_finnetuning', num_labels=5, output_attentions=False, output_hidden_states=False) pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True) classificationResult = pipe("El objetivo de esta tesis es elaborar un estudio de las condiciones asociadas al aprendizaje desde casa") ``` To cite this resource in a publication please use the following: ## Citation [UNAM's Tesis with PlanTL-GOB-ES/roberta-large-bne ](https://huggingface.co/hackathon-pln-es/unam_tesis_ROBERTA_GOB_finnetuning) To cite this resource in a publication please use the following: ``` @inproceedings{SpanishNLPHackaton2022, title={Unam's thesis with PlanTL-GOB-ES/roberta-large-bne classify }, author={López López, Isaac Isaías and López Ramos, Dionis and Clavel Quintero, Yisel and López López, Ximena Yeraldin }, booktitle={Somos NLP Hackaton 2022}, year={2022} } ``` ## Team members - Isaac Isaías López López ([MajorIsaiah](https://huggingface.co/MajorIsaiah)) - Dionis López Ramos ([inoid](https://huggingface.co/inoid)) - Yisel Clavel Quintero ([clavel](https://huggingface.co/clavel)) - Ximyer Yeraldin López López ([Ximyer](https://huggingface.co/Ximyer))
jjeamin/ArcaneStyleTransfer
jjeamin
2022-04-04T01:57:26Z
0
4
null
[ "pytorch", "onnx", "license:apache-2.0", "region:us" ]
null
2022-03-31T11:34:03Z
--- license: apache-2.0 ---
somosnlp-hackathon-2022/bertin-roberta-base-finetuning-esnli
somosnlp-hackathon-2022
2022-04-04T01:45:21Z
74
7
sentence-transformers
[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "es", "dataset:hackathon-pln-es/nli-es", "arxiv:1908.10084", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-28T19:08:33Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: - es datasets: - hackathon-pln-es/nli-es widget: - text: "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos." - text: "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario." - text: "Tendremos que optar por hacer una huelga para cobrar lo que queremos." - text: "Queda descartada la huelga aunque no cobremos lo que queramos." --- # bertin-roberta-base-finetuning-esnli This is a [sentence-transformers](https://www.SBERT.net) model trained on a collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Based around the siamese networks approach from [this paper](https://arxiv.org/pdf/1908.10084.pdf). <!--- Describe your model here --> You can see a demo for this model [here](https://huggingface.co/spaces/hackathon-pln-es/Sentence-Embedding-Bertin). You can find our other model, **paraphrase-spanish-distilroberta** [here](https://huggingface.co/hackathon-pln-es/paraphrase-spanish-distilroberta) and its demo [here](https://huggingface.co/spaces/hackathon-pln-es/Paraphrase-Bertin). ## 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 = ["Este es un ejemplo", "Cada oración es transformada"] model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli') 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('hackathon-pln-es/bertin-roberta-base-finetuning-esnli') model = AutoModel.from_pretrained('hackathon-pln-es/bertin-roberta-base-finetuning-esnli') # 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, mean 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 --> Our model was evaluated on the task of Semantic Textual Similarity using the [SemEval-2015 Task](https://alt.qcri.org/semeval2015/task2/) for [Spanish](http://alt.qcri.org/semeval2015/task2/data/uploads/sts2015-es-test.zip). We measure | | [BETO STS](https://huggingface.co/espejelomar/sentece-embeddings-BETO) | BERTIN STS (this model) | Relative improvement | |-------------------:|---------:|-----------:|---------------------:| | cosine_pearson | 0.609803 | 0.683188 | +12.03 | | cosine_spearman | 0.528776 | 0.615916 | +16.48 | | euclidean_pearson | 0.590613 | 0.672601 | +13.88 | | euclidean_spearman | 0.526529 | 0.611539 | +16.15 | | manhattan_pearson | 0.589108 | 0.672040 | +14.08 | | manhattan_spearman | 0.525910 | 0.610517 | +16.09 | | dot_pearson | 0.544078 | 0.600517 | +10.37 | | dot_spearman | 0.460427 | 0.521260 | +13.21 | ## Training The model was trained with the parameters: **Dataset** We used a collection of datasets of Natural Language Inference as training data: - [ESXNLI](https://raw.githubusercontent.com/artetxem/esxnli/master/esxnli.tsv), only the part in spanish - [SNLI](https://nlp.stanford.edu/projects/snli/), automatically translated - [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/), automatically translated The whole dataset used is available [here](https://huggingface.co/datasets/hackathon-pln-es/nli-es). Here we leave the trick we used to increase the amount of data for training here: ``` for row in reader: if row['language'] == 'es': sent1 = row['sentence1'].strip() sent2 = row['sentence2'].strip() add_to_samples(sent1, sent2, row['gold_label']) add_to_samples(sent2, sent1, row['gold_label']) #Also add the opposite ``` **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 1818 with parameters: ``` {'batch_size': 64} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 909, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel (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}) ) ``` ## Authors [Anibal Pérez](https://huggingface.co/Anarpego), [Emilio Tomás Ariza](https://huggingface.co/medardodt), [Lautaro Gesuelli](https://huggingface.co/Lgesuelli) y [Mauricio Mazuecos](https://huggingface.co/mmazuecos).
abdelhalim/Rec_Business_Names
abdelhalim
2022-04-04T01:39:41Z
15
4
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "Text2Text Generation", "Business names", "Recommendation system", "dataset:BSD-1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-23T13:25:14Z
--- datasets: - BSD-1 tags: - Text2Text Generation - Business names - Recommendation system metrics: - Rouge --- **Context** Most of the business name generator systems based on Rule based approach and only take as input a name or keyword not context. The present trained model its aim is to take in a summary for a business idea (1-2 sentences, could be even keywords) and generate a viable business name for users. **Introduction** The goal is to create an AI service which is helpful to people and yet could turn into a small business. After fiddling around with T5, I have realized it has an immense creative potential that could prove useful in creative text generation. So, after scraping around 350.000 websites from different Domain list, I have fine-tuned T5 small parameter on this dataset. Results are much depends to the context and creative at the same time. T5 small is already pre-trained language model which is capable of creating text with a near human quality. It's able to understand the context of a given prefix to generate text. When fine tuned based on the domain names and their meta context, it was able to understand the relation between domain name and the content of the website. **Dataset** t5 small needs lots of data to be trained properly. Quality of the data that we will use for fine tuning will have a direct effect on the model quality therefore we need to make sure the data we are scraping from the websites are as clean as possible. The dateset will be under request. # Usage In order to use the model in your Python script just copy the following code: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("abdelhalim/Rec_Business_Names") model = AutoModelForSeq2SeqLM.from_pretrained("abdelhalim/Rec_Business_Names") encoder_input_str = "fourniture and decor brand" number_of_business_names = 10 input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids outputs = model.generate( input_ids, num_beams=number_of_business_names, num_return_sequences=number_of_business_names, no_repeat_ngram_size=1, remove_invalid_values=True, ) for i in range(len(outputs)): print(tokenizer.decode(outputs[i], skip_special_tokens=True)) #Output edgy.com Furnace.com Decorsy.com Furnacea.com Decorse.com Furniture.com edgys.com Furnishing.com Lavender.com edgya.com ```
hamedkhaledi/persain-flair-upos
hamedkhaledi
2022-04-03T22:15:00Z
29
0
flair
[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fa", "dataset:ontonotes", "region:us" ]
token-classification
2022-03-25T07:27:51Z
--- tags: - flair - token-classification - sequence-tagger-model language: - fa datasets: - ontonotes widget: - text: "مقامات مصری به خاطر حفظ ثبات کشور در منطقهای پرآشوب بر خود میبالند ، در حالی که این کشور در طول ۱۶ سال گذشته تنها هشت سال آنرا بدون اعلام وضعیت اضطراری سپری کرده است ." --- ## Persian Universal Part-of-Speech Tagging in Flair This is the universal part-of-speech tagging model for Persian that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **97,73** (UD_PERSIAN) Predicts Universal POS tags: | **tag** | **meaning** | |:---------------------------------:|:-----------:| |ADJ | adjective | | ADP | adposition | | ADV | adverb | | AUX | auxiliary | | CCONJ | coordinating conjunction | | DET | determiner | | INTJ | interjection | | NOUN | noun | | NUM | numeral | | PART | particle | | PRON | pronoun | | PUNCT | punctuation | | SCONJ | subordinating conjunction | | VERB | verb | | X | other | --- ### Demo: How to use in Flair Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("hamedkhaledi/persain-flair-upos") # make example sentence sentence = Sentence("مقامات مصری به خاطر حفظ ثبات کشور در منطقهای پرآشوب بر خود میبالند .") tagger.predict(sentence) #print result print(sentence.to_tagged_string()) ``` This yields the following output: ``` مقامات <NOUN> مصری <ADJ> به <ADP> خاطر <NOUN> حفظ <NOUN> ثبات <NOUN> کشور <NOUN> در <ADP> منطقهای <NOUN> پرآشوب <ADJ> بر <ADP> خود <PRON> میبالند <VERB> . <PUNCT> ``` --- ### Results - F-score (micro) 0.9773 - F-score (macro) 0.9461 - Accuracy 0.9773 ``` By class: precision recall f1-score support NOUN 0.9770 0.9849 0.9809 6420 ADP 0.9947 0.9916 0.9932 1909 ADJ 0.9342 0.9128 0.9234 1525 PUNCT 1.0000 1.0000 1.0000 1365 VERB 0.9840 0.9711 0.9775 1141 CCONJ 0.9912 0.9937 0.9925 794 AUX 0.9622 0.9799 0.9710 546 PRON 0.9751 0.9865 0.9808 517 SCONJ 0.9797 0.9757 0.9777 494 NUM 0.9948 1.0000 0.9974 385 ADV 0.9343 0.9033 0.9185 362 DET 0.9773 0.9711 0.9742 311 PART 0.9916 1.0000 0.9958 237 INTJ 0.8889 0.8000 0.8421 10 X 0.7143 0.6250 0.6667 8 micro avg 0.9773 0.9773 0.9773 16024 macro avg 0.9533 0.9397 0.9461 16024 weighted avg 0.9772 0.9773 0.9772 16024 samples avg 0.9773 0.9773 0.9773 16024 Loss: 0.12471389770507812 ```
BigSalmon/InformalToFormalLincoln34
BigSalmon
2022-04-03T20:41:44Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-03T20:17:27Z
``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln34") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln34") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. *** - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. *** - ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. *** Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. *** Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. *** Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. *** Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. *** Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about *** Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers *** Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of *** Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone *** Search: What are phrase synonyms for " ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. *** original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. *** wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. *** sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. *** 1: ``` Keywords to sentences or sentence.
Suhail/Upside_down_detector
Suhail
2022-04-03T17:55:07Z
0
0
null
[ "region:us" ]
null
2022-04-03T14:23:47Z
This model uses images of cats to detect if an image of a cat is upside down or not. <br> I have used fastai library for this. <br> I have collected data on my google drive through colab by using duckduckgo search API <br> I used transfer learning by implementing resnet-18 architecture to solve this particular task.
morahil/wav2vec2-large-xls-r-300m-hindi
morahil
2022-04-03T17:28:16Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-04-03T16:45:22Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hindi 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. --> # wav2vec2-large-xls-r-300m-hindi 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 dataset. ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset
Giyaseddin
2022-04-03T16:39:39Z
93
1
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "en", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-03T14:52:37Z
--- license: gpl-3.0 language: en library: transformers other: distilbert datasets: - Fake and real news dataset --- # DistilBERT base cased model for Fake News Classification ## Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. 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 using the BERT base model. This is a Fake News classification model finetuned [pretrained DistilBERT model](https://huggingface.co/distilbert-base-cased) on [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) ## Intended uses & limitations This can only be used for the kind of news that are similar to the ones in the dataset, please visit the [dataset's kaggle page](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) to see the data. ### How to use You can use this model directly with a : ```python >>> from transformers import pipeline >>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset", return_all_scores=True) >>> examples = ["Yesterday, Speaker Paul Ryan tweeted a video of himself on the Mexican border flying in a helicopter and traveling on horseback with US border agents. RT if you agree It is time for The Wall. pic.twitter.com/s5MO8SG7SL Paul Ryan (@SpeakerRyan) August 1, 2017It makes for great theater to see Republican Speaker Ryan pleading the case for a border wall, but how sincere are the GOP about building the border wall? Even after posting a video that appears to show Ryan s support for the wall, he still seems unsure of himself. It s almost as though he s testing the political winds when he asks Twitter users to retweet if they agree that we need to start building the wall. How committed is the (formerly?) anti-Trump Paul Ryan to building the border wall that would fulfill one of President Trump s most popular campaign promises to the American people? Does he have the what it takes to defy the wishes of corporate donors and the US Chamber of Commerce, and do the right thing for the national security and well-being of our nation?The Last Refuge- Republicans are in control of the House of Representatives, Republicans are in control of the Senate, a Republican President is in the White House, and somehow there s negotiations on how to fund the #1 campaign promise of President Donald Trump, the border wall.Here s the rub.Here s what pundits never discuss.The Republican party doesn t need a single Democrat to fund the border wall.A single spending bill could come from the House of Representatives that fully funds 100% of the border wall. The spending bill then goes to the senate, where again, it doesn t need a single Democrat vote because spending legislation is specifically what reconciliation was designed to facilitate. That House bill can pass the Senate with 51 votes and proceed directly to the President s desk for signature.So, ask yourself: why is this even a point of discussion?The honest answer, for those who are no longer suffering from Battered Conservative Syndrome, is that Republicans don t want to fund or build an actual physical barrier known as the Southern Border Wall.It really is that simple.If one didn t know better, they d almost think Speaker Ryan was attempting to emulate the man he clearly despised during the 2016 presidential campaign."] >>> classifier(examples) [[{'label': 'LABEL_0', 'score': 1.0}, {'label': 'LABEL_1', 'score': 1.0119109106199176e-08}]] ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). This bias will also affect all fine-tuned versions of this model. ## Pre-training data DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Fine-tuning data [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) ## Training procedure ### Preprocessing In the preprocessing phase, both the title and the text of the news are concatenated using a separator `[SEP]`. This makes the full text as: ``` [CLS] Title Sentence [SEP] News text body [SEP] ``` The data are splitted according to the following ratio: - Training set 60%. - Validation set 20%. - Test set 20%. Lables are mapped as: `{fake: 0, true: 1}` ### Fine-tuning The model was finetuned on GeForce GTX 960M for 5 hours. The parameters are: | Parameter | Value | |:-------------------:|:-----:| | Learning rate | 5e-5 | | Weight decay | 0.01 | | Training batch size | 4 | | Epochs | 3 | Here is the scores during the training: | Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | |:----------:|:-------------:|:-----------------:|:----------:|:---------:|:-----------:|:---------:| | 1 | 0.008300 | 0.005783 | 0.998330 | 0.998252 | 0.996511 | 1.000000 | | 2 | 0.000000 | 0.000161 | 0.999889 | 0.999883 | 0.999767 | 1.000000 | | 3 | 0.000000 | 0.000122 | 0.999889 | 0.999883 | 0.999767 | 1.000000 | ## Evaluation results When fine-tuned on downstream task of fake news binary classification, this model achieved the following results: (scores are rounded to 2 floating points) | | precision | recall | f1-score | support | |:------------:|:---------:|:------:|:--------:|:-------:| | Fake | 1.00 | 1.00 | 1.00 | 4697 | | True | 1.00 | 1.00 | 1.00 | 4283 | | accuracy | - | - | 1.00 | 8980 | | macro avg | 1.00 | 1.00 | 1.00 | 8980 | | weighted avg | 1.00 | 1.00 | 1.00 | 8980 | Confision matrix: | Actual\Predicted | Fake | True | |:-----------------:|:----:|:----:| | Fake | 4696 | 1 | | True | 1 | 4282 | The AUC score is 0.9997
AykeeSalazar/violation-classification-bantai-vit-v100ep
AykeeSalazar
2022-04-03T16:16:07Z
64
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:image_folder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-04-03T14:05:38Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy model-index: - name: violation-classification-bantai-vit-v100ep results: - task: name: Image Classification type: image-classification dataset: name: image_folder type: image_folder args: default metrics: - name: Accuracy type: accuracy value: 0.9157343919162757 --- <!-- 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. --> # violation-classification-bantai-vit-v100ep This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.2557 - Accuracy: 0.9157 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2811 | 1.0 | 101 | 0.2855 | 0.9027 | | 0.2382 | 2.0 | 202 | 0.2763 | 0.9085 | | 0.2361 | 3.0 | 303 | 0.2605 | 0.9109 | | 0.196 | 4.0 | 404 | 0.2652 | 0.9110 | | 0.1395 | 5.0 | 505 | 0.2648 | 0.9134 | | 0.155 | 6.0 | 606 | 0.2656 | 0.9152 | | 0.1422 | 7.0 | 707 | 0.2607 | 0.9141 | | 0.1511 | 8.0 | 808 | 0.2557 | 0.9157 | | 0.1938 | 9.0 | 909 | 0.2679 | 0.9049 | | 0.2094 | 10.0 | 1010 | 0.2392 | 0.9137 | | 0.1835 | 11.0 | 1111 | 0.2400 | 0.9156 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
somosnlp-hackathon-2022/roberta-base-biomedical-es-squad2-es
somosnlp-hackathon-2022
2022-04-03T14:51:38Z
6
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "es", "dataset:squad_es", "dataset:hackathon-pln-es/biomed_squad_es_v2", "endpoints_compatible", "region:us" ]
question-answering
2022-04-02T18:25:38Z
--- language: es datasets: - squad_es - hackathon-pln-es/biomed_squad_es_v2 metrics: - "f1" --- # roberta-base-biomedical-es for QA This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP. ## Motivation Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explores the use of these new models to generate extractive Question Answering models for Biomedicine, and compares their effectiveness with general masked language models. The models trained during the [Hackathon](https://somosnlp.org/hackathon) were: [hackathon-pln-es/roberta-base-bne-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-bne-squad2-es) [hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es) [hackathon-pln-es/roberta-base-biomedical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es) [hackathon-pln-es/biomedtra-small-es-squad2-es](https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es) ## Description This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es) on the [squad_es (v2)](https://huggingface.co/datasets/squad_es) training dataset. ## Hyperparameters The hyperparameters were chosen based on those used in [PlanTL-GOB-ES/roberta-base-bne-sqac](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac), a spanish-based QA model trained on a dataset with SQUAD v1 fromat. ``` --num_train_epochs 2 --learning_rate 3e-5 --weight_decay 0.01 --max_seq_length 386 --doc_stride 128 ``` ## Performance Evaluated on the [hackathon-pln-es/biomed_squad_es_v2](https://huggingface.co/datasets/hackathon-pln-es/biomed_squad_es_v2) dev set. |Model |Base Model Domain|exact |f1 |HasAns_exact|HasAns_f1|NoAns_exact|NoAns_f1| |--------------------------------------------------------------|-----------------|-------|-------|------------|---------|-----------|--------| |hackathon-pln-es/roberta-base-bne-squad2-es |General |67.6341|75.6988|53.7367 |70.0526 |81.2174 |81.2174 | |hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es|Biomedical |66.8426|75.2346|53.0249 |70.0031 |80.3478 |80.3478 | |hackathon-pln-es/roberta-base-biomedical-es-squad2-es |Biomedical |67.6341|74.5612|47.6868 |61.7012 |87.1304 | 87.1304| |hackathon-pln-es/biomedtra-small-es-squad2-es |Biomedical |34.4767|44.3294|45.3737 |65.307 |23.8261 |23.8261 | ## Team Santiago Maximo: [smaximo](https://huggingface.co/smaximo)
jsunster/distilbert-base-uncased-finetuned-squad
jsunster
2022-04-03T14:46:14Z
8
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-04-03T13:02:11Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad 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. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1476 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2823 | 1.0 | 2767 | 1.1980 | | 1.0336 | 2.0 | 5534 | 1.1334 | | 0.8513 | 3.0 | 8301 | 1.1476 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6
tonyalves/output
tonyalves
2022-04-03T14:24:57Z
5
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "pt", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-04-01T00:34:39Z
--- language: - pt license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer datasets: - common_voice model-index: - name: output 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. --> # output This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset. It achieves the following results on the evaluation set: - Loss: 0.1505 - Wer: 0.1352 ## 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: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.1367 | 0.64 | 500 | 3.8825 | 1.0 | | 2.9677 | 1.29 | 1000 | 2.9498 | 1.0 | | 1.5884 | 1.93 | 1500 | 0.6722 | 0.6493 | | 1.2292 | 2.57 | 2000 | 0.3635 | 0.3202 | | 1.1314 | 3.22 | 2500 | 0.2970 | 0.2680 | | 1.0879 | 3.86 | 3000 | 0.2671 | 0.2486 | | 1.0344 | 4.5 | 3500 | 0.2625 | 0.2239 | | 1.0109 | 5.15 | 4000 | 0.2520 | 0.2230 | | 0.9966 | 5.79 | 4500 | 0.2280 | 0.2105 | | 0.9815 | 6.43 | 5000 | 0.2254 | 0.2179 | | 0.9744 | 7.08 | 5500 | 0.2301 | 0.2137 | | 0.9487 | 7.72 | 6000 | 0.2224 | 0.2051 | | 0.9431 | 8.37 | 6500 | 0.2105 | 0.1992 | | 0.9365 | 9.01 | 7000 | 0.2114 | 0.2019 | | 0.9268 | 9.65 | 7500 | 0.2097 | 0.1988 | | 0.9292 | 10.3 | 8000 | 0.2120 | 0.1986 | | 0.929 | 10.94 | 8500 | 0.2048 | 0.1998 | | 0.9017 | 11.58 | 9000 | 0.2035 | 0.1999 | | 0.8898 | 12.23 | 9500 | 0.1961 | 0.1908 | | 0.8799 | 12.87 | 10000 | 0.1945 | 0.1817 | | 0.869 | 13.51 | 10500 | 0.1929 | 0.1844 | | 0.8572 | 14.16 | 11000 | 0.1941 | 0.1888 | | 0.8691 | 14.8 | 11500 | 0.1912 | 0.1804 | | 0.8645 | 15.44 | 12000 | 0.1950 | 0.1851 | | 0.8468 | 16.09 | 12500 | 0.1879 | 0.1770 | | 0.8405 | 16.73 | 13000 | 0.1881 | 0.1759 | | 0.8647 | 17.37 | 13500 | 0.1861 | 0.1740 | | 0.8477 | 18.02 | 14000 | 0.1782 | 0.1702 | | 0.811 | 18.66 | 14500 | 0.1915 | 0.1757 | | 0.8165 | 19.3 | 15000 | 0.1820 | 0.1724 | | 0.8166 | 19.95 | 15500 | 0.1798 | 0.1697 | | 0.8167 | 20.59 | 16000 | 0.1805 | 0.1752 | | 0.7908 | 21.24 | 16500 | 0.1761 | 0.1699 | | 0.7925 | 21.88 | 17000 | 0.1740 | 0.1709 | | 0.7803 | 22.52 | 17500 | 0.1815 | 0.1727 | | 0.7839 | 23.17 | 18000 | 0.1737 | 0.1694 | | 0.7815 | 23.81 | 18500 | 0.1732 | 0.1630 | | 0.767 | 24.45 | 19000 | 0.1724 | 0.1648 | | 0.7672 | 25.1 | 19500 | 0.1706 | 0.1596 | | 0.7691 | 25.74 | 20000 | 0.1718 | 0.1618 | | 0.7547 | 26.38 | 20500 | 0.1694 | 0.1565 | | 0.7498 | 27.03 | 21000 | 0.1706 | 0.1582 | | 0.7459 | 27.67 | 21500 | 0.1663 | 0.1586 | | 0.7374 | 28.31 | 22000 | 0.1651 | 0.1567 | | 0.7499 | 28.96 | 22500 | 0.1668 | 0.1549 | | 0.7471 | 29.6 | 23000 | 0.1667 | 0.1553 | | 0.7369 | 30.24 | 23500 | 0.1659 | 0.1556 | | 0.7389 | 30.89 | 24000 | 0.1668 | 0.1538 | | 0.7197 | 31.53 | 24500 | 0.1687 | 0.1561 | | 0.71 | 32.17 | 25000 | 0.1666 | 0.1516 | | 0.7199 | 32.82 | 25500 | 0.1640 | 0.1523 | | 0.7194 | 33.46 | 26000 | 0.1659 | 0.1528 | | 0.6923 | 34.11 | 26500 | 0.1662 | 0.1507 | | 0.7054 | 34.75 | 27000 | 0.1641 | 0.1486 | | 0.6955 | 35.39 | 27500 | 0.1634 | 0.1497 | | 0.7084 | 36.04 | 28000 | 0.1618 | 0.1478 | | 0.6917 | 36.68 | 28500 | 0.1589 | 0.1471 | | 0.687 | 37.32 | 29000 | 0.1589 | 0.1450 | | 0.6914 | 37.97 | 29500 | 0.1588 | 0.1465 | | 0.6646 | 38.61 | 30000 | 0.1602 | 0.1468 | | 0.6667 | 39.25 | 30500 | 0.1588 | 0.1444 | | 0.6754 | 39.9 | 31000 | 0.1587 | 0.1455 | | 0.6632 | 40.54 | 31500 | 0.1586 | 0.1461 | | 0.6619 | 41.18 | 32000 | 0.1571 | 0.1441 | | 0.6561 | 41.83 | 32500 | 0.1564 | 0.1420 | | 0.6492 | 42.47 | 33000 | 0.1539 | 0.1437 | | 0.6649 | 43.11 | 33500 | 0.1512 | 0.1406 | | 0.6511 | 43.76 | 34000 | 0.1539 | 0.1384 | | 0.6551 | 44.4 | 34500 | 0.1520 | 0.1384 | | 0.6452 | 45.05 | 35000 | 0.1510 | 0.1368 | | 0.6155 | 45.69 | 35500 | 0.1522 | 0.1375 | | 0.628 | 46.33 | 36000 | 0.1522 | 0.1366 | | 0.6389 | 46.97 | 36500 | 0.1513 | 0.1377 | | 0.6265 | 47.62 | 37000 | 0.1512 | 0.1369 | | 0.6197 | 48.26 | 37500 | 0.1511 | 0.1362 | | 0.621 | 48.91 | 38000 | 0.1510 | 0.1357 | | 0.6259 | 49.55 | 38500 | 0.1506 | 0.1353 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.1+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
AnnaBabaie/ms-marco-MiniLM-L-12-v2-news
AnnaBabaie
2022-04-03T13:46:51Z
3
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-03T12:55:06Z
This model is fined tuned for the Fake news classifier: Train a text classification model to detect fake news articles. Base on the Kaggle dataset(https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset).
alefiury/wav2vec2-xls-r-300m-pt-br-spontaneous-speech-emotion-recognition
alefiury
2022-04-03T12:38:09Z
66
6
transformers
[ "transformers", "pytorch", "wav2vec2", "audio-classification", "audio", "speech", "pt", "portuguese-speech-corpus", "italian-speech-corpus", "english-speech-corpus", "arabic-speech-corpus", "spontaneous", "PyTorch", "dataset:coraa_ser", "dataset:emovo", "dataset:ravdess", "dataset:baved", "license:apache-2.0", "endpoints_compatible", "region:us" ]
audio-classification
2022-03-23T15:29:36Z
--- language: pt datasets: - coraa_ser - emovo - ravdess - baved metrics: - f1 tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - italian-speech-corpus - english-speech-corpus - arabic-speech-corpus - spontaneous - speech - PyTorch license: apache-2.0 model_index: name: wav2vec2-xls-r-300m-pt-br-spontaneous-speech-emotion-recognition results: metrics: - name: Test Macro F1-Score type: f1 value: 81.87% --- # Wav2vec 2.0 XLS-R For Spontaneous Speech Emotion Recognition This is the model that got first place in the SER track of the Automatic Speech Recognition for spontaneous and prepared speech & Speech Emotion Recognition in Portuguese (SE&R 2022) Workshop. The following datasets were used in the training: - [CORAA SER v1.0](https://github.com/rmarcacini/ser-coraa-pt-br/): a dataset composed of spontaneous portuguese speech and approximately 40 minutes of audio segments labeled in three classes: neutral, non-neutral female, and non-neutral male. - [EMOVO Corpus](https://aclanthology.org/L14-1478/): a database of emotional speech for the Italian language, built from the voices of up to 6 actors who played 14 sentences simulating 6 emotional states (disgust, fear, anger, joy, surprise, sadness) plus the neutral state. - [RAVDESS](https://zenodo.org/record/1188976#.YO6yI-gzaUk): a dataset that provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are: angry, calm, disgust, fearful, happy, neutral, sad and surprised. - [BAVED](https://github.com/40uf411/Basic-Arabic-Vocal-Emotions-Dataset): a collection of audio recordings of Arabic words spoken with varying degrees of emotion. The dataset contains seven words: like, unlike, this, file, good, neutral, and bad, which are spoken at three emotional levels: low emotion (tired or feeling down), neutral emotion (the way the speaker speaks daily), and high emotion (positive or negative emotions such as happiness, joy, sadness, anger). The test set used is a part of the CORAA SER v1.0 that has been set aside for this purpose. It achieves the following results on the test set: - Accuracy: 0.9090 - Macro Precision: 0.8171 - Macro Recall: 0.8397 - Macro F1-Score: 0.8187 ## Datasets Details The following image shows the overall distribution of the datasets: ![distribution](https://docs.google.com/spreadsheets/d/e/2PACX-1vTUvuMLRnoFv3MBkStOcMQE5GuiqqyrvpyEtIiwoQEg8uA6dWvfZM-faHORLFNmPYJUzDbO6TZ2a9Zb/pubchart?oid=446282973&format=image) The following image shows the number of instances by label: ![numberInstances](https://docs.google.com/spreadsheets/d/e/2PACX-1vS7PUbW6J3Hnof1D2l492KW0sbF4BzWCeaiGQm53w-9EZck_Y14feE48HtcBvmjjZKsTJWP1RZpdh_v/pubchart?oid=1904097403&format=image) ## Repository The repository that implements the model to be trained and tested is avaible [here](https://github.com/alefiury/SE-R-2022-SER-Track).
AykeeSalazar/violation-classification-bantai_vit
AykeeSalazar
2022-04-03T12:26:48Z
62
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:image_folder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-04-03T03:01:22Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder model-index: - name: violation-classification-bantai_vit 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. --> # violation-classification-bantai_vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2362 - eval_accuracy: 0.9478 - eval_runtime: 43.2567 - eval_samples_per_second: 85.42 - eval_steps_per_second: 2.682 - epoch: 87.0 - step: 10005 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 500 ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Zohar/distilgpt2-finetuned-restaurant-reviews-clean
Zohar
2022-04-03T10:29:27Z
3
1
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-03T07:25:35Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-restaurant-reviews-clean 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. --> # distilgpt2-finetuned-restaurant-reviews-clean This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5371 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7221 | 1.0 | 2447 | 3.5979 | | 3.6413 | 2.0 | 4894 | 3.5505 | | 3.6076 | 3.0 | 7341 | 3.5371 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.11.0
AykeeSalazar/vit-base-patch16-224-in21k-bantai_vitv1
AykeeSalazar
2022-04-03T02:43:41Z
63
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:image_folder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-04-02T14:17:18Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-bantai_vitv1 results: - task: name: Image Classification type: image-classification dataset: name: image_folder type: image_folder args: default metrics: - name: Accuracy type: accuracy value: 0.8635994587280108 --- <!-- 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. --> # vit-base-patch16-224-in21k-bantai_vitv1 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.3961 - Accuracy: 0.8636 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5997 | 1.0 | 115 | 0.5401 | 0.7886 | | 0.4696 | 2.0 | 230 | 0.4410 | 0.8482 | | 0.4019 | 3.0 | 345 | 0.3961 | 0.8636 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/clortown-elonmusk-stephencurry30
huggingtweets
2022-04-02T23:03:14Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-02T23:02:39Z
--- language: en thumbnail: http://www.huggingtweets.com/clortown-elonmusk-stephencurry30/1648940589601/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1488574779351187458/RlIQNUFG_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1484233608793518081/tOID8aXq_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & yeosang elf agenda & Stephen Curry</div> <div style="text-align: center; font-size: 14px;">@clortown-elonmusk-stephencurry30</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & yeosang elf agenda & Stephen Curry. | Data | Elon Musk | yeosang elf agenda | Stephen Curry | | --- | --- | --- | --- | | Tweets downloaded | 221 | 3143 | 3190 | | Retweets | 7 | 541 | 384 | | Short tweets | 62 | 463 | 698 | | Tweets kept | 152 | 2139 | 2108 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2sqcbnn5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @clortown-elonmusk-stephencurry30's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1mq1ftjh) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1mq1ftjh/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/clortown-elonmusk-stephencurry30') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
vicl/canine-s-finetuned-cola
vicl
2022-04-02T23:01:51Z
3
1
transformers
[ "transformers", "pytorch", "tensorboard", "canine", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-02T22:29:20Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: canine-s-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.059386434587477076 --- <!-- 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. --> # canine-s-finetuned-cola This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6653 - Matthews Correlation: 0.0594 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6132 | 1.0 | 535 | 0.6289 | 0.0 | | 0.6062 | 2.0 | 1070 | 0.6179 | 0.0 | | 0.6122 | 3.0 | 1605 | 0.6160 | 0.0 | | 0.5939 | 4.0 | 2140 | 0.6159 | 0.0 | | 0.5721 | 5.0 | 2675 | 0.6653 | 0.0594 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
vocab-transformers/distilbert-mlm-best
vocab-transformers
2022-04-02T21:18:53Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-04-02T21:18:48Z
distilbert-base-uncased trained for 680K steps (lowest loss on dev dataset) with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
vocab-transformers/distilbert-mlm-750k
vocab-transformers
2022-04-02T21:15:27Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-04-02T21:15:23Z
distilbert-base-uncased trained for 750K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
vocab-transformers/distilbert-mlm-500k
vocab-transformers
2022-04-02T21:12:46Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-04-02T21:12:40Z
distilbert-base-uncased trained for 500K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
vocab-transformers/distilbert-mlm-250k
vocab-transformers
2022-04-02T21:10:59Z
5
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-04-02T21:07:10Z
distilbert-base-uncased trained for 250K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
somosnlp-hackathon-2022/paraphrase-spanish-distilroberta
somosnlp-hackathon-2022
2022-04-02T18:33:17Z
4,608
15
sentence-transformers
[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "es", "dataset:hackathon-pln-es/parallel-sentences", "arxiv:2004.09813", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-30T17:58:23Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language: - es datasets: - hackathon-pln-es/parallel-sentences widget: - text: "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos." - text: "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario." - text: "Tendremos que optar por hacer una huelga para cobrar lo que queremos." - text: "Queda descartada la huelga aunque no cobremos lo que queramos." --- # paraphrase-spanish-distilroberta 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. We follow a **teacher-student** transfer learning approach to train an `bertin-roberta-base-spanish` model using parallel EN-ES sentence pairs. ## 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 = ["Este es un ejemplo", "Cada oración es transformada"] model = SentenceTransformer('hackathon-pln-es/paraphrase-spanish-distilroberta') 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 import torch.nn.functional as F #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 = ['Este es un ejemplo", "Cada oración es transformada'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/paraphrase-spanish-distilroberta') model = AutoModel.from_pretrained('hackathon-pln-es/paraphrase-spanish-distilroberta') # 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 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (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}) ) ``` ## Evaluation Results Similarity Evaluation on STS-2017.es-en.txt and STS-2017.es-es.txt (translated manually for evaluation purposes) We measure the semantic textual similarity (STS) between sentence pairs in different languages: ### ES-ES | cosine_pearson | cosine_spearman | manhattan_pearson | manhattan_spearman | euclidean_pearson | euclidean_spearman | dot_pearson | dot_spearman | | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | 0.8495 | 0.8579 | 0.8675 | 0.8474 | 0.8676 | 0.8478 | 0.8277 | 0.8258 | ### ES-EN | cosine_pearson | cosine_spearman | manhattan_pearson | manhattan_spearman | euclidean_pearson | euclidean_spearman | dot_pearson | dot_spearman | | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | 0.8344 | 0.8448 | 0.8279 | 0.8168 | 0.8282 | 0.8159 | 0.8083 | 0.8145 | ------ ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. ## Background This model is a bilingual Spanish-English model trained according to instructions in the paper [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/pdf/2004.09813.pdf) and the [documentation](https://www.sbert.net/examples/training/multilingual/README.html) accompanying its companion python package. We have used the strongest available pretrained English Bi-Encoder ([paraphrase-mpnet-base-v2](https://www.sbert.net/docs/pretrained_models.html#sentence-embedding-models)) as a teacher model, and the pretrained Spanish [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) as the student model. We developped this model during the [Hackathon 2022 NLP - Spanish](https://somosnlp.org/hackathon), organized by hackathon-pln-es Organization. ### Training data We use the concatenation from multiple datasets with sentence pairs (EN-ES). We could check out the dataset that was used during training: [parallel-sentences](https://huggingface.co/datasets/hackathon-pln-es/parallel-sentences) | Dataset | |--------------------------------------------------------| | AllNLI - ES (SNLI + MultiNLI)| | EuroParl | | JW300 | | News Commentary | | Open Subtitles | | TED 2020 | | Tatoeba | | WikiMatrix | ## Authors - [Anibal Pérez](https://huggingface.co/Anarpego), - [Emilio Tomás Ariza](https://huggingface.co/medardodt), - [Lautaro Gesuelli Pinto](https://huggingface.co/lautaro) - [Mauricio Mazuecos](https://huggingface.co/mmazuecos)
JustAdvanceTechonology/medical_notes_mulitilingual
JustAdvanceTechonology
2022-04-02T16:37:24Z
4
0
transformers
[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-04-02T11:06:15Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: JustAdvanceTechonology/medical_notes_mulitilingual 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. --> # JustAdvanceTechonology/medical_notes_mulitilingual This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.7536 - Validation Loss: 6.1397 - Epoch: 7 ## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 1209, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 11.2097 | 6.1454 | 0 | | 8.7069 | 6.1880 | 1 | | 8.7350 | 6.1834 | 2 | | 8.7021 | 6.1364 | 3 | | 8.7385 | 6.2117 | 4 | | 8.7318 | 6.2004 | 5 | | 8.7487 | 6.1531 | 6 | | 8.7536 | 6.1397 | 7 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.5.0 - Datasets 2.0.0 - Tokenizers 0.10.1
jaygala24/finetuned-vit-base-patch16-224-upside-down-detector
jaygala24
2022-04-02T15:24:57Z
79
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "accelerator", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-04-02T08:42:45Z
--- license: apache-2.0 tags: - accelerator metrics: - accuracy model-index: - name: finetuned-vit-base-patch16-224-upside-down-detector results: [] widget: - src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/original.jpg example_title: original - src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/upside_down.jpg example_title: upside_down --- # finetuned-vit-base-patch16-224-upside-down-detector This model is a fine-tuned version of [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom image orientation dataset adapted from the [beans](https://huggingface.co/datasets/beans) dataset. It achieves the following results on the evaluation set: - Accuracy: 0.8947 ## Training and evaluation data The custom dataset for image orientation adapted from [beans](https://huggingface.co/datasets/beans) dataset contains a total of 2,590 image samples with 1,295 original and 1,295 upside down. The model was fine-tuned on the train subset and evaluated on validation and test subsets. The dataset splits are listed below: | Split | # examples | |:----------:|:----------:| | train | 2068 | | validation | 133 | | test | 128 | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-04 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32 - num_epochs: 5 ### Training results | Epoch | Accuracy | |:----------:|:----------:| | 0 | 0.8609 | | 1 | 0.8835 | | 2 | 0.8571 | | 3 | 0.8941 | | 4 | 0.8941 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.0+cu111 - Pytorch/XLA 1.9 - Datasets 2.0.0 - Tokenizers 0.12.0
yaswanth/distilbert-base-uncased_fakenews_identification
yaswanth
2022-04-02T13:18:07Z
7
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-31T06:10:15Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased_fakenews_identification 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. --> # distilbert-base-uncased_fakenews_identification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the below dataset. https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset It achieves the following results on the evaluation set: - Loss: 0.0059 - Accuracy: 0.999 - F1: 0.9990 ## Label Description LABEL_0 - Fake News LABEL_1 - Real News ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0014 | 1.0 | 1000 | 0.0208 | 0.9965 | 0.9965 | | 0.0006 | 2.0 | 2000 | 0.0041 | 0.9994 | 0.9994 | | 0.0006 | 3.0 | 3000 | 0.0044 | 0.9992 | 0.9993 | | 0.0 | 4.0 | 4000 | 0.0059 | 0.999 | 0.9990 | ### Framework versions - Transformers 4.16.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Sam4669/distilbert-base-uncased-finetuned-emotion
Sam4669
2022-04-02T13:16:26Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-02T13:00:45Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9232158277556175 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2317 - Accuracy: 0.923 - F1: 0.9232 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8528 | 1.0 | 250 | 0.3332 | 0.897 | 0.8929 | | 0.26 | 2.0 | 500 | 0.2317 | 0.923 | 0.9232 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
OmarAlasqa/RotNet_FatimaFellowship
OmarAlasqa
2022-04-02T12:45:33Z
0
0
null
[ "tensorboard", "region:us" ]
null
2022-04-02T10:31:43Z
**Upside down detector**: Train a model to detect if images are upside down * Trained on Google Street View. * Synthetically turn some of images upside down. Create a training and test set. * Build a neural network using TensorFlow. * Train it to classify image orientation until a reasonable accuracy is reached. * Look at some of the images that were classified incorrectly. Please explain what you might do to improve your model's performance on these images in the future. *The code is taken from: [RotNet](https://github.com/d4nst/RotNet), with minor changes.*
anuragshas/en-hi-transliteration
anuragshas
2022-04-02T12:24:03Z
0
1
null
[ "license:apache-2.0", "region:us" ]
null
2022-04-02T11:50:28Z
--- license: apache-2.0 --- ## Dataset [NEWS2018 DATASET_04, Task ID: M-EnHi](http://workshop.colips.org/news2018/dataset.html) ## Notebooks - `xmltodict.ipynb` contains the code to convert the `xml` files to `json` for training - `training_script.ipynb` contains the code for training and inference. It is a modified version of https://github.com/AI4Bharat/IndianNLP-Transliteration/blob/master/NoteBooks/Xlit_TrainingSetup_condensed.ipynb ## Predictions `pred_test.json` contains top-10 predictions on the validation set of the dataset ## Evaluation Scores on validation set TOP 10 SCORES FOR 1000 SAMPLES |Metrics | Score | |-----------|-----------| |ACC | 0.703000| |Mean F-score| 0.949289| |MRR | 0.486549| |MAP_ref | 0.381000| TOP 5 SCORES FOR 1000 SAMPLES: |Metrics | Score | |-----------|-----------| |ACC |0.621000| |Mean F-score |0.937985| |MRR |0.475033| |MAP_ref |0.381000| TOP 3 SCORES FOR 1000 SAMPLES: |Metrics | Score | |-----------|-----------| |ACC |0.560000| |Mean F-score |0.927025| |MRR |0.461333| |MAP_ref |0.381000| TOP 2 SCORES FOR 1000 SAMPLES: |Metrics | Score | |-----------|-----------| |ACC | 0.502000| |Mean F-score | 0.913697| |MRR | 0.442000| |MAP_ref | 0.381000| TOP 1 SCORES FOR 1000 SAMPLES: |Metrics | Score | |-----------|-----------| |ACC | 0.382000| |Mean F-score | 0.881272| |MRR | 0.382000| |MAP_ref | 0.380500|
huggingtweets/sanjabh
huggingtweets
2022-04-02T12:14:56Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-02T12:13:25Z
--- language: en thumbnail: http://www.huggingtweets.com/sanjabh/1648901691950/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1484080880222351360/FtDB2j4B_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Lucid Dreams</div> <div style="text-align: center; font-size: 14px;">@sanjabh</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Lucid Dreams. | Data | Lucid Dreams | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 373 | | Short tweets | 137 | | Tweets kept | 2740 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s7tzf32/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sanjabh's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1cl1cjnx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1cl1cjnx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/sanjabh') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
DMetaSoul/sbert-chinese-qmc-domain-v1-distill
DMetaSoul
2022-04-02T10:03:06Z
6
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-04-02T10:02:53Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - semantic-search - chinese --- # DMetaSoul/sbert-chinese-qmc-domain-v1 此模型是基于之前开源[问题匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-domain-v1)的蒸馏轻量化版本(仅含4层 BERT),适用于**开放领域的问题匹配**场景,比如: - 洗澡用什么香皂好?vs. 洗澡用什么香皂好 - 大连哪里拍婚纱照好点? vs. 大连哪里拍婚纱照比较好 - 银行卡怎样挂失?vs. 银行卡丢了怎么挂失啊? 离线训练好的大模型如果直接用于线上推理,对计算资源有苛刻的需求,而且难以满足业务环境对延迟、吞吐量等性能指标的要求,这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后,模型参数量缩小到 44%,大概 latency 减半、throughput 翻倍、精度下降 4% 左右(具体结果详见下文评估小节)。 # Usage ## 1. Sentence-Transformers 通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型,首先进行安装: ``` pip install -U sentence-transformers ``` 然后使用下面的代码来载入该模型并进行文本表征向量的提取: ```python from sentence_transformers import SentenceTransformer sentences = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"] model = SentenceTransformer('DMetaSoul/sbert-chinese-qmc-domain-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## 2. HuggingFace Transformers 如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话,也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取: ```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 = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1') model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1') # 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, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation 这里主要跟蒸馏前对应的 teacher 模型作了对比 *性能:* | | Teacher | Student | Gap | | ---------- | --------------------- | ------------------- | ----- | | Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x | | Cost | 23s | 12s | -47% | | Latency | 38ms | 20ms | -47% | | Throughput | 421 sentence/s | 791 sentence/s | 1.9x | *精度:* | | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** | | -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- | | **Teacher** | 80.90% | 76.62% | 34.51% | 77.05% | 52.95% | 12.97% | 59.47% | 56.35% | | **Student** | 79.89% | 76.34% | 27.59% | 69.26% | 49.40% | 9.06% | 53.52% | 52.15% | | **Gap** (abs.) | - | - | - | - | - | - | - | -4.2% | *基于1万条数据测试,GPU设备是V100,batch_size=16,max_seq_len=256* ## Citing & Authors E-mail: [email protected]
DMetaSoul/sbert-chinese-general-v2-distill
DMetaSoul
2022-04-02T09:58:33Z
15
6
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-04-02T09:58:18Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - semantic-search - chinese --- # DMetaSoul/sbert-chinese-general-v2-distill 此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2)的蒸馏版本(仅4层 BERT),适用于**通用语义匹配**场景,从效果来看该模型在各种任务上**泛化能力更好且编码速度更快**。 离线训练好的大模型如果直接用于线上推理,对计算资源有苛刻的需求,而且难以满足业务环境对延迟、吞吐量等性能指标的要求,这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后,模型参数量缩小到 44%,大概 latency 减半、throughput 翻倍、精度下降 6% 左右(具体结果详见下文评估小节)。 # Usage ## 1. Sentence-Transformers 通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型,首先进行安装: ``` pip install -U sentence-transformers ``` 然后使用下面的代码来载入该模型并进行文本表征向量的提取: ```python from sentence_transformers import SentenceTransformer sentences = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"] model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v2-distill') embeddings = model.encode(sentences) print(embeddings) ``` ## 2. HuggingFace Transformers 如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话,也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取: ```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 = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v2-distill') model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v2-distill') # 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, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation 这里主要跟蒸馏前对应的 teacher 模型作了对比: *性能:* | | Teacher | Student | Gap | | ---------- | --------------------- | ------------------- | ----- | | Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x | | Cost | 23s | 12s | -47% | | Latency | 38ms | 20ms | -47% | | Throughput | 418 sentence/s | 791 sentence/s | 1.9x | *精度:* | | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** | | -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- | | **Teacher** | 77.19% | 72.59% | 36.79% | 76.91% | 49.62% | 16.24% | 63.15% | 56.07% | | **Student** | 76.49% | 73.33% | 26.46% | 64.26% | 46.02% | 11.83% | 52.45% | 50.12% | | **Gap** (abs.) | - | - | - | - | - | - | - | -5.95% | *基于1万条数据测试,GPU设备是V100,batch_size=16,max_seq_len=256* ## Citing & Authors E-mail: [email protected]
Chikashi/t5-small-finetuned-wikihow_3epoch
Chikashi
2022-04-02T07:42:15Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wikihow", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-04-01T21:20:33Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikihow metrics: - rouge model-index: - name: t5-small-finetuned-wikihow_3epoch results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wikihow type: wikihow args: all metrics: - name: Rouge1 type: rouge value: 25.5784 --- <!-- 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-wikihow_3epoch This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.5163 - Rouge1: 25.5784 - Rouge2: 8.9929 - Rougel: 21.5345 - Rougelsum: 24.9382 - Gen Len: 18.384 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.9421 | 0.25 | 5000 | 2.6545 | 23.2336 | 7.5502 | 19.5899 | 22.5521 | 18.4076 | | 2.8411 | 0.51 | 10000 | 2.6103 | 24.3524 | 8.2068 | 20.5238 | 23.6679 | 18.2606 | | 2.7983 | 0.76 | 15000 | 2.5836 | 24.8169 | 8.4826 | 20.8765 | 24.1686 | 18.3211 | | 2.7743 | 1.02 | 20000 | 2.5627 | 24.9904 | 8.5625 | 21.0344 | 24.3416 | 18.3786 | | 2.7452 | 1.27 | 25000 | 2.5508 | 25.1497 | 8.6872 | 21.152 | 24.4751 | 18.3524 | | 2.7353 | 1.53 | 30000 | 2.5384 | 25.2909 | 8.7408 | 21.2344 | 24.629 | 18.4453 | | 2.7261 | 1.78 | 35000 | 2.5322 | 25.3748 | 8.7802 | 21.312 | 24.7191 | 18.3754 | | 2.7266 | 2.03 | 40000 | 2.5265 | 25.4095 | 8.8915 | 21.3871 | 24.7685 | 18.4013 | | 2.706 | 2.29 | 45000 | 2.5211 | 25.4372 | 8.8926 | 21.4124 | 24.7902 | 18.3776 | | 2.7073 | 2.54 | 50000 | 2.5176 | 25.4925 | 8.9668 | 21.5103 | 24.8608 | 18.4303 | | 2.703 | 2.8 | 55000 | 2.5163 | 25.5784 | 8.9929 | 21.5345 | 24.9382 | 18.384 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Suman123/upside-down-detector
Suman123
2022-04-02T07:33:31Z
0
0
null
[ "region:us" ]
null
2022-04-01T12:56:45Z
TASK 1 of Faltima Fellowship- UpsideDown detector
satoshiz01/Flipped_CIFAR10_vision
satoshiz01
2022-04-02T05:09:07Z
0
0
null
[ "region:us" ]
null
2022-04-02T03:30:55Z
**Google Colab Notebook link:** https://colab.research.google.com/drive/1iA8nvb93VLcrDfIt17AOIHnkVdLSNcW_?usp=sharing This repo contains files for defining and creating a simple convolutional network for classifying/detecting the orientation of CIFAR-10 images (either normal orientation or flipped upside down/180 degrees). The following files are in this repo: Coding_Challenge_for_Fatima_Fellowship.ipynb -- a copy of the Google Collab notebook with the code/output/writeup best_model.pth -- dictionary of best model stats/weights found during training cifar10flip_trn.pt -- saved training dataset of ~50% flipped CIFAR10 images cifar10flip_tst.pt -- saved training dataset of ~50% flipped CIFAR10 images image_examples.png -- an array of example imags from flipped CIFAR10 dataset write-up -- write up of data processing, model results, and potential improvements (also in Google Colab) wrong_predictions.zip -- a zip file of PNG images that were incorrectly classified by my model (each file name provide information on the image's prediction, true label, and its class)
nikhil6041/wav2vec2-commonvoice-hindi
nikhil6041
2022-04-02T04:48:26Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-31T04:27:46Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-commonvoice-hindi 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. --> # wav2vec2-commonvoice-hindi This model is a fine-tuned version of [theainerd/Wav2Vec2-large-xlsr-hindi](https://huggingface.co/theainerd/Wav2Vec2-large-xlsr-hindi) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9825 - Wer: 0.6763 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 20.0 | 100 | 0.8801 | 0.6754 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
BigSalmon/Points4
BigSalmon
2022-04-02T03:04:08Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-02T02:57:31Z
``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Points4") model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points4") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. *** - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. *** - ``` It should also be able to do all that this can: https://huggingface.co/BigSalmon/InformalToFormalLincoln27 Keywords to sentences or sentence.
TheJarmanitor/fatima-fellowship-model
TheJarmanitor
2022-04-02T03:03:42Z
0
0
null
[ "region:us" ]
null
2022-04-02T03:01:06Z
model and notebook for the Fatima Fellowship 2022 coding Challenge
youssefadarrab/TP_NLP_SNLI_Adarrab_Baziz_Malige
youssefadarrab
2022-04-02T00:40:26Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-01T21:11:05Z
# CentraleSupelec - Natural language processing # Practical session n°7 ## Natural Language Inferencing (NLI): (NLI) is a classical NLP (Natural Language Processing) problem that involves taking two sentences (the premise and the hypothesis ), and deciding how they are related (if the premise *entails* the hypothesis, *contradicts* it, or *neither*). Ex: | Premise | Label | Hypothesis | | --- | --- | --- | | A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. | | An older and younger man smiling. | neutral | Two men are smiling and laughing at the cats playing on the floor. | | A soccer game with multiple males playing. | entailment | Some men are playing a sport. | ### Stanford NLI (SNLI) corpus In this labwork, I propose to use the Stanford NLI (SNLI) corpus ( https://nlp.stanford.edu/projects/snli/ ), available in the *Datasets* library by Huggingface. from datasets import load_dataset snli = load_dataset("snli") #Removing sentence pairs with no label (-1) snli = snli.filter(lambda example: example['label'] != -1) ## Quick summary of the model This is the model from : Youssef Adarrab, Othmane Baziz and Alain Malige - Fist we import the corpus and do some visualization - Second we apply DistilBert for sequence classification - We illustrate through our work the code used for training, to obtain better results, one should run the training on more epochs
JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en
JustAdvanceTechonology
2022-04-02T00:07:29Z
4
0
transformers
[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-31T10:16:30Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en 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. --> # JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6429 - Validation Loss: 0.8071 - Epoch: 2 ## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6423 | 0.8071 | 0 | | 0.6424 | 0.8071 | 1 | | 0.6429 | 0.8071 | 2 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.5.0 - Datasets 2.0.0 - Tokenizers 0.10.1
vicl/canine-s-finetuned-stsb
vicl
2022-04-01T23:25:04Z
4
1
transformers
[ "transformers", "pytorch", "tensorboard", "canine", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-01T19:47:18Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: canine-s-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8397182061195433 --- <!-- 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. --> # canine-s-finetuned-stsb This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7223 - Pearson: 0.8397 - Spearmanr: 0.8397 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.7938 | 0.8083 | 0.8077 | | 1.278 | 2.0 | 720 | 0.7349 | 0.8322 | 0.8305 | | 0.6765 | 3.0 | 1080 | 0.7075 | 0.8374 | 0.8366 | | 0.6765 | 4.0 | 1440 | 0.7586 | 0.8360 | 0.8376 | | 0.4629 | 5.0 | 1800 | 0.7223 | 0.8397 | 0.8397 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/chapocheck
huggingtweets
2022-04-01T22:07:43Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-04-01T22:06:55Z
--- language: en thumbnail: http://www.huggingtweets.com/chapocheck/1648850858747/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1191821996759404547/HY5C5aOW_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Cum Town (mostly Nick Mullen) quotes</div> <div style="text-align: center; font-size: 14px;">@chapocheck</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Cum Town (mostly Nick Mullen) quotes. | Data | Cum Town (mostly Nick Mullen) quotes | | --- | --- | | Tweets downloaded | 1264 | | Retweets | 90 | | Short tweets | 75 | | Tweets kept | 1099 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/x77h239f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @chapocheck's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/18r1isa5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/18r1isa5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/chapocheck') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
lgris/bp500-base10k_voxpopuli
lgris
2022-04-01T20:34:35Z
5
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "pt", "portuguese-speech-corpus", "PyTorch", "hf-asr-leaderboard", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "arxiv:2012.03411", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch - hf-asr-leaderboard model-index: - name: bp500-base10k_voxpopuli results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice type: common_voice args: pt metrics: - name: Test WER type: wer value: 24.9 license: apache-2.0 --- # bp500-base10k_voxpopuli: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets: - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus. - [Common Voice 7.0](https://commonvoice.mozilla.org/pt): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt). - [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control. - [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers. - [Multilingual TEDx](http://www.openslr.org/100): a collection of audio recordings from TEDx talks in 8 source languages. The Portuguese set (mostly Brazilian Portuguese variant) contains 164 hours of transcribed speech. - [Sidney](https://igormq.github.io/datasets/) (SID): contains 5,777 utterances recorded by 72 speakers (20 women) from 17 to 59 years old with fields such as place of birth, age, gender, education, and occupation; - [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz. These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively. We also made test sets for all the gathered datasets. | Dataset | Train | Valid | Test | |--------------------------------|-------:|------:|------:| | CETUC | 94.0h | -- | 5.4h | | Common Voice | 37.8h | 8.9h | 9.5h | | LaPS BM | 0.8h | -- | 0.1h | | MLS | 161.0h | -- | 3.7h | | Multilingual TEDx (Portuguese) | 148.9h | -- | 1.8h | | SID | 7.2h | -- | 1.0h | | VoxForge | 3.9h | -- | 0.1h | | Total | 453.6h | 8.9h | 21.6h | The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/file/d/19kkENi8uvczmw9OLSdqnjvKqBE53cl_W/view?usp=sharing). #### Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | bp\_500-base10k_voxpopuli (demonstration below) | 0.120 | 0.249 | 0.039 | 0.227 | 0.169 | 0.349 | 0.116 | 0.181 | | bp\_500-base10k_voxpopuli + 4-gram (demonstration below) | 0.074 | 0.174 | 0.032 | 0.182 | 0.181 | 0.349 | 0.111 | 0.157 | #### Transcription examples | Text | Transcription | |------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------| |suco de uva e água misturam bem|suco **deúva** e água **misturão** bem| |culpa do dinheiro|**cupa** do dinheiro| |eu amo shooters call of duty é o meu favorito|eu **omo** **shúters cofedete** é meu favorito| |você pode explicar por que isso acontece|você pode explicar *por* que isso **ontece**| |no futuro você desejará ter começado a investir hoje|no futuro você desejará **a** ter começado a investir hoje| ## Demonstration ```python MODEL_NAME = "lgris/bp500-base10k_voxpopuli" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) with torch.no_grad(): logits = self.model(input_values).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ```python %cd bp_dataset ``` /content/bp_dataset ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.12096759949218888 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.24977003159495725 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.039769570707070705 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.2269637077788063 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.1691680138494731 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.34908555859018014 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.11649350649350651 ### Tests with LM ```python !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` ### Cetuc ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.07499558425787961 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.17442648452610307 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.032774621212121206 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.18213620321569274 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.18102544972868206 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.3491402028105601 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.11189529220779222
lgris/bp500-xlsr
lgris
2022-04-01T20:33:47Z
15
1
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "pt", "portuguese-speech-corpus", "PyTorch", "hf-asr-leaderboard", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "arxiv:2012.03411", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch - hf-asr-leaderboard model-index: - name: bp400-xlsr results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice type: common_voice args: pt metrics: - name: Test WER type: wer value: 13.6 license: apache-2.0 --- # bp500-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets: - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus; - [Common Voice 7.0](https://commonvoice.mozilla.org/pt): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt); - [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control; - [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers; - [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz. These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively. We also made test sets for all the gathered datasets. | Dataset | Train | Valid | Test | |--------------------------------|-------:|------:|------:| | CETUC | 93.9h | -- | 5.4h | | Common Voice | 37.6h | 8.9h | 9.5h | | LaPS BM | 0.8h | -- | 0.1h | | MLS | 161.0h | -- | 3.7h | | Multilingual TEDx (Portuguese) | 144.2h | -- | 1.8h | | SID | 5.0h | -- | 1.0h | | VoxForge | 2.8h | -- | 0.1h | | Total | 437.2h | 8.9h | 21.6h | The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/file/d/1J8aR1ltDLQFe-dVrGuyxoRm2uyJjCWgf/view?usp=sharing). #### Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | bp\_500 (demonstration below) | 0.051 | 0.136 | 0.032 | 0.118 | 0.095 | 0.248 | 0.082 | 0.108 | | bp\_500 + 4-gram (demonstration below) | 0.032 | 0.097 | 0.022 | 0.114 | 0.125 | 0.246 | 0.065 | 0.100 | #### Transcription examples | Text | Transcription | |------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------| |não há um departamento de mediadores independente das federações e das agremiações|não há um **dearamento** de mediadores independente das federações e das **agrebiações**| |mas que bodega|**masque** bodega| |a cortina abriu o show começou|a cortina abriu o **chô** começou| |por sorte havia uma passadeira|**busote avinhoa** **passadeiro**| |estou maravilhada está tudo pronto|**stou** estou maravilhada está tudo pronto| ## Demonstration ```python MODEL_NAME = "lgris/bp500-xlsr" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) attention_mask = features.attention_mask.to(self.device) with torch.no_grad(): logits = self.model(input_values, attention_mask=attention_mask).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ```python %cd bp_dataset ``` /content/bp_dataset ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.05159097808687998 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.13659981509705973 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.03196969696969697 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.1178481066463896 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.09544588416964224 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.24868046340420813 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.08246076839826841 ### Tests with LM ```python !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` ### Cetuc ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.03222801788375573 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.09713866021093655 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.022310606060606065 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.11408590958696524 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.12502797252979136 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.24603179403904793 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.06542207792207791
lgris/bp400-xlsr
lgris
2022-04-01T20:31:02Z
91
3
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "pt", "portuguese-speech-corpus", "PyTorch", "hf-asr-leaderboard", "dataset:common_voice", "dataset:mls", "dataset:cetuc", "dataset:lapsbm", "dataset:voxforge", "dataset:tedx", "dataset:sid", "arxiv:2107.11414", "arxiv:2012.03411", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: pt datasets: - common_voice - mls - cetuc - lapsbm - voxforge - tedx - sid metrics: - wer tags: - audio - speech - wav2vec2 - pt - portuguese-speech-corpus - automatic-speech-recognition - speech - PyTorch - hf-asr-leaderboard model-index: - name: bp400-xlsr results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7.0 type: mozilla-foundation/common_voice_7_0 args: pt metrics: - name: Test WER type: wer value: 14.0 license: apache-2.0 --- # bp400-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset **Paper:** https://arxiv.org/abs/2107.11414 This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets: - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus. - [Common Voice 7.0](https://commonvoice.mozilla.org/pt): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt). - [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control. - [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers. - [Multilingual TEDx](http://www.openslr.org/100): a collection of audio recordings from TEDx talks in 8 source languages. The Portuguese set (mostly Brazilian Portuguese variant) contains 164 hours of transcribed speech. - [Sidney](https://igormq.github.io/datasets/) (SID): contains 5,777 utterances recorded by 72 speakers (20 women) from 17 to 59 years old with fields such as place of birth, age, gender, education, and occupation; - [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz. These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively. We also made test sets for all the gathered datasets. | Dataset | Train | Valid | Test | |--------------------------------|-------:|------:|------:| | CETUC | 93.9h | -- | 5.4h | | Common Voice | 37.6h | 8.9h | 9.5h | | LaPS BM | 0.8h | -- | 0.1h | | MLS | 161.0h | -- | 3.7h | | Multilingual TEDx (Portuguese) | 144.2h | -- | 1.8h | | SID | 5.0h | -- | 1.0h | | VoxForge | 2.8h | -- | 0.1h | | Total | 437.2h | 8.9h | 21.6h | The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/drive/folders/1eRUExXRF2XK8JxUjIzbLBkLa5wuR3nig?usp=sharing). #### Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | bp\_400 (demonstration below) | 0.052 | 0.140 | 0.074 | 0.117 | 0.121 | 0.245 | 0.118 | 0.124 | | bp\_400 + 3-gram | 0.033 | 0.095 | 0.046 | 0.123 | 0.112 | 0.212 | 0.123 | 0.106 | | bp\_400 + 4-gram (demonstration below) | **0.030** | 0.096 | 0.043 | **0.106** | 0.118 | 0.229 | **0.117** | **0.105** | | bp\_400 + 5-gram | 0.033 | 0.094 | 0.043 | 0.123 | **0.111** | **0.210** | 0.123 | **0.105** | | bp\_400 + Transf. | 0.032 | **0.092** | **0.036** | 0.130 | 0.115 | 0.215 | 0.125 | 0.106 | #### Transcription examples | Text | Transcription | |------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------| |alguém sabe a que horas começa o jantar | alguém sabe a que horas **começo** jantar | |lila covas ainda não sabe o que vai fazer no fundo|**lilacovas** ainda não sabe o que vai fazer no fundo| |que tal um pouco desse bom spaghetti|**quetá** um pouco **deste** bom **ispaguete**| |hong kong em cantonês significa porto perfumado|**rongkong** **en** **cantones** significa porto perfumado| |vamos hackear esse problema|vamos **rackar** esse problema| |apenas a poucos metros há uma estação de ônibus|apenas **ha** poucos metros **á** uma estação de ônibus| |relâmpago e trovão sempre andam juntos|**relampagotrevão** sempre andam juntos| ## Demonstration ```python MODEL_NAME = "lgris/bp400-xlsr" ``` ### Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: # Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` ### Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) attention_mask = features.attention_mask.to(self.device) with torch.no_grad(): logits = self.model(input_values, attention_mask=attention_mask).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ### Tests ```python stt = STT(MODEL_NAME) ``` #### CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.05159104708285062 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.14031426198658084 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.07432133838383838 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.11678793514817509 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.12152357273433984 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.24666815906766504 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.11873106060606062 ### Tests with LM ```python !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` ### Cetuc ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.030266462438593742 #### Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.09577710237417715 #### LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.043617424242424235 #### MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.10642133314350002 #### SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.11839021001747055 #### TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.22929952467810416 #### VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.11716314935064935
juaner/distilbert-base-uncased-finetuned-cola
juaner
2022-04-01T18:20:42Z
5
0
transformers
[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-04-01T17:59:52Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: juaner/distilbert-base-uncased-finetuned-cola 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. --> # juaner/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1909 - Validation Loss: 0.5553 - Train Matthews Correlation: 0.5279 - Epoch: 2 ## 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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5191 | 0.4491 | 0.4718 | 0 | | 0.3270 | 0.4571 | 0.5196 | 1 | | 0.1909 | 0.5553 | 0.5279 | 2 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0
FrankCorrigan/results
FrankCorrigan
2022-04-01T18:15:40Z
3
0
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-04-01T01:41:22Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum model-index: - name: results 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. --> # results This model is a fine-tuned version of [linydub/bart-large-samsum](https://huggingface.co/linydub/bart-large-samsum) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.0158 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 0.9563 | | No log | 2.0 | 2 | 0.9877 | | No log | 3.0 | 3 | 1.0158 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0 - Datasets 2.0.0 - Tokenizers 0.11.6
FrankCorrigan/test-model
FrankCorrigan
2022-04-01T17:54:00Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2022-04-01T01:46:45Z
--- license: apache-2.0 ---