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librarian-bots
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lucianpopa/autonlp-SST1-529214890
lucianpopa
2022-01-25T17:30:09Z
3
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autonlp", "en", "dataset:lucianpopa/autonlp-data-SST1", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - lucianpopa/autonlp-data-SST1 co2_eq_emissions: 49.618294309910624 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 529214890 - CO2 Emissions (in grams): 49.618294309910624 ## Validation Metrics - Loss: 0.7135734558105469 - Accuracy: 0.7042338838232481 - Macro F1: 0.6164041045783032 - Micro F1: 0.7042338838232481 - Weighted F1: 0.7028309161791009 - Macro Precision: 0.6497438111060598 - Micro Precision: 0.7042338838232481 - Weighted Precision: 0.7076651075198755 - Macro Recall: 0.6023419083862918 - Micro Recall: 0.7042338838232481 - Weighted Recall: 0.7042338838232481 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/lucianpopa/autonlp-SST1-529214890 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("lucianpopa/autonlp-SST1-529214890", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("lucianpopa/autonlp-SST1-529214890", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
anirudh21/electra-base-discriminator-finetuned-rte
anirudh21
2022-01-25T15:43:18Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "electra", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: electra-base-discriminator-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.8231046931407943 --- <!-- 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. --> # electra-base-discriminator-finetuned-rte This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4793 - Accuracy: 0.8231 ## 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 156 | 0.6076 | 0.6570 | | No log | 2.0 | 312 | 0.4824 | 0.7762 | | No log | 3.0 | 468 | 0.4793 | 0.8231 | | 0.4411 | 4.0 | 624 | 0.7056 | 0.7906 | | 0.4411 | 5.0 | 780 | 0.6849 | 0.8159 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
huggingtweets/arryadia_brk
huggingtweets
2022-01-25T14:04:36Z
3
0
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: http://www.huggingtweets.com/arryadia_brk/1643119471683/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/1479498403251896320/uDVlO62z_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">الرياضية - عاجل</div> <div style="text-align: center; font-size: 14px;">@arryadia_brk</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 الرياضية - عاجل. | Data | الرياضية - عاجل | | --- | --- | | Tweets downloaded | 1548 | | Retweets | 11 | | Short tweets | 33 | | Tweets kept | 1504 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24udtdhw/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 @arryadia_brk's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2e36ahiu) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2e36ahiu/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/arryadia_brk') 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)
dhanesh123in/layoutlmv2-finetuned-funsd-test
dhanesh123in
2022-01-25T12:33:29Z
7
0
transformers
[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer model-index: - name: layoutlmv2-finetuned-funsd-test 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. --> # layoutlmv2-finetuned-funsd-test This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown 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: 5e-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 - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1 - Datasets 1.18.0 - Tokenizers 0.11.0
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c_inference_only
deepdoctection
2022-01-25T09:23:24Z
0
0
null
[ "Tensorflow", "dataset:Pubtabnet", "arxiv:1911.10683", "license:apache-2.0", "region:us" ]
null
2022-03-02T23:29:05Z
--- tags: - Tensorflow license: apache-2.0 datasets: - Pubtabnet --- # Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . Regarding the dataset, please check: [Xu Zhong et. all. - Image-based table recognition: data, model, and evaluation](https://arxiv.org/abs/1911.10683). The model has been trained on detecting cells from tables. Note, that the datasets contains tables only. Therefore, it is required to perform a table detection task before detecting cells. The code has been adapted so that it can be used in a **deep**doctection pipeline. ## How this model can be used This model can be used with the **deep**doctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this [Get_started](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Get_Started.ipynb) tutorial. ## This is an inference model only To reduce the size of the checkpoint we removed all variables that are not necessary for inference. Therefore it cannot be used for fine-tuning. To fine tune this model please check this [model](https://huggingface.co/deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c) . ## How this model was trained. To recreate the model run on the **deep**doctection framework, run: ```python >>> import os >>> from deep_doctection.datasets import DatasetRegistry >>> from deep_doctection.eval import MetricRegistry >>> from deep_doctection.utils import get_configs_dir_path >>> from deep_doctection.train import train_faster_rcnn pubtabnet = DatasetRegistry.get_dataset("pubtabnet") pubtabnet.dataflow.categories.filter_categories(categories="CELL") path_config_yaml=os.path.join(get_configs_dir_path(),"tp/cell/conf_frcnn_cell.yaml") path_weights = "" dataset_train = pubtabnet config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.STARTING_EPOCH=1", "TRAIN.CHECKPOINT_PERIOD=50","BACKBONE.FREEZE_AT=0", "PREPROC.TRAIN_SHORT_EDGE_SIZE=[200,600]"] build_train_config=["max_datapoints=500000"] dataset_val = pubtabnet build_val_config = ["max_datapoints=4000"] coco_metric = MetricRegistry.get_metric("coco") coco_metric.set_params(max_detections=[50,200,600], area_range=[[0,1000000],[0,200],[200,800],[800,1000000]]) train_faster_rcnn(path_config_yaml=path_config_yaml, dataset_train=dataset_train, path_weights=path_weights, config_overwrite=config_overwrite, log_dir="/path/to/dir", build_train_config=build_train_config, dataset_val=dataset_val, build_val_config=build_val_config, metric=coco_metric, pipeline_component_name="ImageLayoutService" ) ``` ## How to fine-tune this model To fine tune this model, please check this [Fine-tune](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Fine_Tune.ipynb) tutorial.
NimaBoscarino/aot-gan-celebahq
NimaBoscarino
2022-01-25T08:38:46Z
4
1
transformers
[ "transformers", "pytorch", "face-recognition", "face-generation", "face-segmentation", "generative-adversarial-network", "dataset:celeba-hq", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:04Z
--- tags: - face-recognition - face-generation - face-segmentation - generative-adversarial-network metrics: - L1 - PSNR - SSIM - FID datasets: - celeba-hq --- # AOT-GAN CelebA-HQ AOT-GAN is a model that can be used for image in-painting. The CelebA-HQ checkpoint is trained on synthetic human faces, which should make it suitable for touching up and restoring portraits. This model was generated using [AOT-GAN-for-Inpainting](https://github.com/researchmm/AOT-GAN-for-Inpainting), cited as ``` @inproceedings{yan2021agg, author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining}, title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting}, booktitle = {Arxiv}, pages={-}, year = {2020} } ``` ## Dataset The CelebA-HQ dataset was created with this codebase: https://github.com/tkarras/progressive_growing_of_gans, owned by NVidia and licensed under Creative Commons Attribution-NonCommercial 4.0 International.
Suva/uptag-url-model
Suva
2022-01-25T04:32:49Z
6
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "dataset:arxiv", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- datasets: - arxiv widget: - text: "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems." license: mit --- ## Usage: ```python abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems. """ ``` ### Using Transformers🤗 ```python model_name = "Suva/uptag-url-model" from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) input_ids = tokenizer.encode("summarize: " + abstract, return_tensors="pt", add_special_tokens=True) generated_ids = model.generate(input_ids=input_ids,num_beams=5,max_length=100,repetition_penalty=2.5,length_penalty=1,early_stopping=True,num_return_sequences=3) preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids] print(preds) # output ["Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers", "Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems", "Overton: Building, Monitoring, and Improving Production Machine Learning Systems"] ```
byeongal/gpt-j-6B-float15
byeongal
2022-01-25T04:25:23Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2022-03-02T23:29:05Z
--- license: apache-2.0 ---
byeongal/gpt-j-6B-float16
byeongal
2022-01-25T03:21:06Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2022-03-02T23:29:05Z
--- license: apache-2.0 ---
shunxing1234/test_model
shunxing1234
2022-01-25T03:14:05Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
Model_name description tutorial
mtglearn/roberta-mtg-cards
mtglearn
2022-01-25T02:57:42Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2022-03-02T23:29:05Z
--- license: apache-2.0 ---
Fengkai/distilbert-base-uncased-finetuned-emotion
Fengkai
2022-01-25T02:11:58Z
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
2022-03-02T23:29:04Z
--- 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: 0.9385 - name: F1 type: f1 value: 0.9383492808338979 --- <!-- 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: 0.1495 - Accuracy: 0.9385 - F1: 0.9383 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1739 | 1.0 | 250 | 0.1827 | 0.931 | 0.9302 | | 0.1176 | 2.0 | 500 | 0.1567 | 0.9325 | 0.9326 | | 0.0994 | 3.0 | 750 | 0.1555 | 0.9385 | 0.9389 | | 0.08 | 4.0 | 1000 | 0.1496 | 0.9445 | 0.9443 | | 0.0654 | 5.0 | 1250 | 0.1495 | 0.9385 | 0.9383 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
aviator-neural/gpt2-donald_trump
aviator-neural
2022-01-24T22:09:58Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-donald_trump results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-donald_trump This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8721 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 391 | 2.8721 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
birgermoell/wav2vec2-common_voice-tr-demo
birgermoell
2022-01-24T18:52:26Z
10
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: - sv-SE license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-common_voice-tr-demo 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-common_voice-tr-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.5528 - Wer: 0.3811 ## 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: 15.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.74 | 100 | 3.4444 | 1.0 | | No log | 1.47 | 200 | 2.9421 | 1.0 | | No log | 2.21 | 300 | 2.2802 | 1.0137 | | No log | 2.94 | 400 | 0.9683 | 0.7611 | | 3.7264 | 3.68 | 500 | 0.7941 | 0.6594 | | 3.7264 | 4.41 | 600 | 0.6695 | 0.5751 | | 3.7264 | 5.15 | 700 | 0.6507 | 0.5314 | | 3.7264 | 5.88 | 800 | 0.5731 | 0.4927 | | 3.7264 | 6.62 | 900 | 0.5723 | 0.4580 | | 0.4592 | 7.35 | 1000 | 0.5913 | 0.4479 | | 0.4592 | 8.09 | 1100 | 0.5562 | 0.4423 | | 0.4592 | 8.82 | 1200 | 0.5566 | 0.4292 | | 0.4592 | 9.56 | 1300 | 0.5492 | 0.4303 | | 0.4592 | 10.29 | 1400 | 0.5665 | 0.4331 | | 0.2121 | 11.03 | 1500 | 0.5610 | 0.4084 | | 0.2121 | 11.76 | 1600 | 0.5703 | 0.4014 | | 0.2121 | 12.5 | 1700 | 0.5669 | 0.3898 | | 0.2121 | 13.24 | 1800 | 0.5586 | 0.3962 | | 0.2121 | 13.97 | 1900 | 0.5656 | 0.3897 | | 0.1326 | 14.71 | 2000 | 0.5565 | 0.3813 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
asanwari/agriculture-sentence-transformer
asanwari
2022-01-24T17:36:27Z
0
0
sentence-transformers
[ "sentence-transformers", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- pipeline_tag: sentence-similarity language: english tags: - sentence-transformers - sentence-similarity - transformers --- # recobo/agri-sentence-transformer This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model was built using [recobo/agriculture-bert-uncased](https://huggingface.co/recobo/agriculture-bert-uncased), which is a BERT model trained on 6.5 million passages from the agricultural domain. Hence, this model is expected to perform well on sentence similarity tasks specifically for agricultural text data. ## 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 = ["A man is eating food.", "A man is eating a piece of bread"] model = SentenceTransformer('recobo/agri-sentence-transformer') embeddings = model.encode(sentences) print(embeddings)
EColi/sponsorblock-base-v1
EColi
2022-01-24T17:23:23Z
5
1
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:04Z
--- tags: - generated_from_trainer model-index: - name: out 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. --> # out This model is a fine-tuned version of [/1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/](https://huggingface.co//1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0645 ## 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: 1 - eval_batch_size: 1 - seed: 2518227880 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | 0.0867 | 0.07 | 75000 | 0.0742 | | 0.0783 | 0.13 | 150000 | 0.0695 | | 0.0719 | 0.2 | 225000 | 0.0732 | | 0.0743 | 0.27 | 300000 | 0.0663 | | 0.0659 | 0.34 | 375000 | 0.0686 | | 0.0664 | 0.4 | 450000 | 0.0683 | | 0.0637 | 0.47 | 525000 | 0.0680 | | 0.0655 | 0.54 | 600000 | 0.0641 | | 0.0676 | 0.6 | 675000 | 0.0644 | | 0.0704 | 0.67 | 750000 | 0.0645 | | 0.0687 | 0.74 | 825000 | 0.0610 | | 0.059 | 0.81 | 900000 | 0.0652 | | 0.0666 | 0.87 | 975000 | 0.0619 | | 0.0624 | 0.94 | 1050000 | 0.0619 | | 0.0625 | 1.01 | 1125000 | 0.0667 | | 0.0614 | 1.03 | 1150000 | 0.0658 | | 0.0597 | 1.05 | 1175000 | 0.0683 | | 0.0629 | 1.07 | 1200000 | 0.0691 | | 0.0603 | 1.1 | 1225000 | 0.0678 | | 0.0601 | 1.12 | 1250000 | 0.0746 | | 0.0606 | 1.14 | 1275000 | 0.0691 | | 0.0671 | 1.16 | 1300000 | 0.0702 | | 0.0625 | 1.19 | 1325000 | 0.0661 | | 0.0617 | 1.21 | 1350000 | 0.0688 | | 0.0579 | 1.23 | 1375000 | 0.0679 | | 0.0663 | 1.25 | 1400000 | 0.0634 | | 0.0583 | 1.28 | 1425000 | 0.0638 | | 0.0623 | 1.3 | 1450000 | 0.0681 | | 0.0615 | 1.32 | 1475000 | 0.0670 | | 0.0592 | 1.34 | 1500000 | 0.0666 | | 0.0626 | 1.37 | 1525000 | 0.0666 | | 0.063 | 1.39 | 1550000 | 0.0647 | | 0.0648 | 1.41 | 1575000 | 0.0653 | | 0.0611 | 1.43 | 1600000 | 0.0700 | | 0.0622 | 1.46 | 1625000 | 0.0634 | | 0.0617 | 1.48 | 1650000 | 0.0651 | | 0.0613 | 1.5 | 1675000 | 0.0634 | | 0.0639 | 1.52 | 1700000 | 0.0661 | | 0.0615 | 1.54 | 1725000 | 0.0644 | | 0.0605 | 1.57 | 1750000 | 0.0662 | | 0.0622 | 1.59 | 1775000 | 0.0656 | | 0.0585 | 1.61 | 1800000 | 0.0633 | | 0.0628 | 1.63 | 1825000 | 0.0625 | | 0.0638 | 1.66 | 1850000 | 0.0662 | | 0.0599 | 1.68 | 1875000 | 0.0664 | | 0.0583 | 1.7 | 1900000 | 0.0668 | | 0.0543 | 1.72 | 1925000 | 0.0631 | | 0.06 | 1.75 | 1950000 | 0.0629 | | 0.0615 | 1.77 | 1975000 | 0.0644 | | 0.0587 | 1.79 | 2000000 | 0.0663 | | 0.0647 | 1.81 | 2025000 | 0.0654 | | 0.0604 | 1.84 | 2050000 | 0.0639 | | 0.0641 | 1.86 | 2075000 | 0.0636 | | 0.0604 | 1.88 | 2100000 | 0.0636 | | 0.0654 | 1.9 | 2125000 | 0.0652 | | 0.0588 | 1.93 | 2150000 | 0.0638 | | 0.0616 | 1.95 | 2175000 | 0.0657 | | 0.0598 | 1.97 | 2200000 | 0.0646 | | 0.0633 | 1.99 | 2225000 | 0.0645 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c
deepdoctection
2022-01-24T16:15:44Z
0
0
null
[ "Tensorflow", "dataset:Pubtabnet", "arxiv:1911.10683", "license:apache-2.0", "region:us" ]
null
2022-03-02T23:29:05Z
--- tags: - Tensorflow license: apache-2.0 datasets: - Pubtabnet --- # Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . Regarding the dataset, please check: [Xu Zhong et. all. - Image-based table recognition: data, model, and evaluation](https://arxiv.org/abs/1911.10683). The model has been trained on detecting cells from tables. Note, that the datasets contains tables only. Therefore, it is required to perform a table detection task before detecting cells. The code has been adapted so that it can be used in a **deep**doctection pipeline. ## How this model can be used This model can be used with the **deep**doctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this [Get_started](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Get_Started.ipynb) tutorial. ## How this model was trained. To recreate the model run on the **deep**doctection framework, run: ```python >>> import os >>> from deep_doctection.datasets import DatasetRegistry >>> from deep_doctection.eval import MetricRegistry >>> from deep_doctection.utils import get_configs_dir_path >>> from deep_doctection.train import train_faster_rcnn pubtabnet = DatasetRegistry.get_dataset("pubtabnet") pubtabnet.dataflow.categories.filter_categories(categories="CELL") path_config_yaml=os.path.join(get_configs_dir_path(),"tp/cell/conf_frcnn_cell.yaml") path_weights = "" dataset_train = pubtabnet config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.STARTING_EPOCH=1", "TRAIN.CHECKPOINT_PERIOD=50","BACKBONE.FREEZE_AT=0", "PREPROC.TRAIN_SHORT_EDGE_SIZE=[200,600]"] build_train_config=["max_datapoints=500000"] dataset_val = pubtabnet build_val_config = ["max_datapoints=4000"] coco_metric = MetricRegistry.get_metric("coco") coco_metric.set_params(max_detections=[50,200,600], area_range=[[0,1000000],[0,200],[200,800],[800,1000000]]) train_faster_rcnn(path_config_yaml=path_config_yaml, dataset_train=dataset_train, path_weights=path_weights, config_overwrite=config_overwrite, log_dir="/path/to/dir", build_train_config=build_train_config, dataset_val=dataset_val, build_val_config=build_val_config, metric=coco_metric, pipeline_component_name="ImageLayoutService" ) ``` ## How to fine-tune this model To fine tune this model, please check this [Fine-tune](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Fine_Tune.ipynb) tutorial.
dbsamu/electra-small-discriminator-finetuned-ner
dbsamu
2022-01-24T14:27:41Z
13
1
transformers
[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "dataset:wikiann", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikiann metrics: - precision - recall - f1 - accuracy model-index: - name: electra-small-discriminator-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: wikiann type: wikiann args: en metrics: - name: Precision type: precision value: 0.7330965535385425 - name: Recall type: recall value: 0.7542632861138681 - name: F1 type: f1 value: 0.7435293071244329 - name: Accuracy type: accuracy value: 0.8883011190233978 --- <!-- 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. --> # electra-small-discriminator-finetuned-ner This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.3685 - Precision: 0.7331 - Recall: 0.7543 - F1: 0.7435 - Accuracy: 0.8883 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.5465 | 1.0 | 1250 | 0.4158 | 0.6932 | 0.7201 | 0.7064 | 0.8735 | | 0.4037 | 2.0 | 2500 | 0.3817 | 0.7191 | 0.7470 | 0.7328 | 0.8828 | | 0.3606 | 3.0 | 3750 | 0.3685 | 0.7331 | 0.7543 | 0.7435 | 0.8883 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet_inference_only
deepdoctection
2022-01-24T13:05:27Z
0
0
null
[ "Tensorflow", "dataset:Publaynet", "arxiv:1908.07836", "license:apache-2.0", "region:us" ]
null
2022-03-02T23:29:05Z
--- tags: - Tensorflow license: apache-2.0 datasets: - Publaynet --- # Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . Please check: [Xu Zhong et. all. - PubLayNet: largest dataset ever for document layout analysis](https://arxiv.org/abs/1908.07836). This model is different from the model used the paper. The code has been adapted so that it can be used in a **deep**doctection pipeline. ## How this model can be used This model can be used with the **deep**doctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this [Get_started](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Get_Started.ipynb) tutorial. ## This is an inference model only To reduce the size of the checkpoint we removed all variables that are not necessary for inference. Therefore it cannot be used for fine-tuning. To fine tune this model please check [this model](https://huggingface.co/deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet). ## How this model was trained. To recreate the model run on the **deep**doctection framework, run: ```python >>> import os >>> from deep_doctection.datasets import DatasetRegistry >>> from deep_doctection.eval import MetricRegistry >>> from deep_doctection.utils import get_configs_dir_path >>> from deep_doctection.train import train_faster_rcnn publaynet = DatasetRegistry.get_dataset("publaynet") path_config_yaml=os.path.join(get_configs_dir_path(),"tp/layout/conf_frcnn_layout.yaml") path_weights = "" dataset_train = publaynet config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.EVAL_PERIOD=200","TRAIN.STARTING_EPOCH=1", "PREPROC.TRAIN_SHORT_EDGE_SIZE=[800,1200]","TRAIN.CHECKPOINT_PERIOD=50", "BACKBONE.FREEZE_AT=0"] build_train_config=["max_datapoints=335703"] dataset_val = publaynet build_val_config = ["max_datapoints=2000"] coco_metric = MetricRegistry.get_metric("coco") train_faster_rcnn(path_config_yaml=path_config_yaml, dataset_train=dataset_train, path_weights=path_weights, config_overwrite=config_overwrite, log_dir="/path/to/dir", build_train_config=build_train_config, dataset_val=dataset_val, build_val_config=build_val_config, metric=coco_metric, pipeline_component_name="ImageLayoutService" ) ```
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet
deepdoctection
2022-01-24T13:02:44Z
0
1
null
[ "Tensorflow", "dataset:Publaynet", "arxiv:1908.07836", "license:apache-2.0", "region:us" ]
null
2022-03-02T23:29:05Z
--- tags: - Tensorflow license: apache-2.0 datasets: - Publaynet --- # Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . Please check: [Xu Zhong et. all. - PubLayNet: largest dataset ever for document layout analysis](https://arxiv.org/abs/1908.07836). This model is different from the model used the paper. The code has been adapted so that it can be used in a **deep**doctection pipeline. ## How this model can be used This model can be used with the **deep**doctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this [Get_started](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Get_Started.ipynb) tutorial. ## How this model was trained. To recreate the model run on the **deep**doctection framework, run: ```python >>> import os >>> from deep_doctection.datasets import DatasetRegistry >>> from deep_doctection.eval import MetricRegistry >>> from deep_doctection.utils import get_configs_dir_path >>> from deep_doctection.train import train_faster_rcnn publaynet = DatasetRegistry.get_dataset("publaynet") path_config_yaml=os.path.join(get_configs_dir_path(),"tp/layout/conf_frcnn_layout.yaml") path_weights = "" dataset_train = publaynet config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.EVAL_PERIOD=200","TRAIN.STARTING_EPOCH=1", "PREPROC.TRAIN_SHORT_EDGE_SIZE=[800,1200]","TRAIN.CHECKPOINT_PERIOD=50", "BACKBONE.FREEZE_AT=0"] build_train_config=["max_datapoints=335703"] dataset_val = publaynet build_val_config = ["max_datapoints=2000"] coco_metric = MetricRegistry.get_metric("coco") train_faster_rcnn(path_config_yaml=path_config_yaml, dataset_train=dataset_train, path_weights=path_weights, config_overwrite=config_overwrite, log_dir="/path/to/dir", build_train_config=build_train_config, dataset_val=dataset_val, build_val_config=build_val_config, metric=coco_metric, pipeline_component_name="ImageLayoutService" ) ``` ## How to fine-tune this model To fine tune this model, please check this [Fine-tune](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Fine_Tune.ipynb) tutorial.
nimelinia/rut5-reply-headline-model
nimelinia
2022-01-24T12:31:54Z
1
0
transformers
[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
This model was trained from rut5-base-multitask with pair of questions and answers (in Russian). The model demonstrate interesting behavior with option "reply" and "headline". When model creates a headline for paragraph of text, it not only uses phrases from text, but also generate new words and sometimes new meanings. Examples of questions and answers: > Как зовут отца Александра Сергеевича Пушкина? > - Пушкин > Где купить вкусное мороженое? > - В супермаркете > Красивая ли Мона Лиза? > - Очень красивая Examples of headlines: > Власти Пекина из-за пандемии COVID-19 призвали жителей города отказаться от помощи и избегать любого контакта с олимпийскими машинами, попавшими в ДТП. Об этом сообщает South China Morning Post. > - Китайский губернатор призвал жителей Пекина отказаться от помощи > Казахский народ должен поддержать своего президента Касым-Жомарт Токаева на фоне угрозы повторения массовых беспорядков, но и властям страны следует провести демократические реформы для снижения недовольства. Об этом в интервью изданию Orda заявил бывший генеральный продюсер гостелеканала «Хабар», экс-глава канала «Ел Арна» Серик Абас-Шах. > - Казахский народ должен поддержать Токаева > Позиция России по макроэкономическим показателям является лучшей в мире. Об этом сказал ТАСС российский исполнительный директор в Международном валютном фонде (МВФ) Алексей Можин. > - Российская экономика является лучшей в мире
philschmid/distilbert-base-multilingual-cased-sentiment
philschmid
2022-01-24T12:14:53Z
6,860
2
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 model-index: - name: distilbert-base-multilingual-cased-sentiment results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi args: all_languages metrics: - name: Accuracy type: accuracy value: 0.7648 - name: F1 type: f1 value: 0.7648 --- <!-- 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-multilingual-cased-sentiment This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.5842 - Accuracy: 0.7648 - F1: 0.7648 ## 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: 33 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.6405 | 0.53 | 5000 | 0.5826 | 0.7498 | 0.7498 | | 0.5698 | 1.07 | 10000 | 0.5686 | 0.7612 | 0.7612 | | 0.5286 | 1.6 | 15000 | 0.5593 | 0.7636 | 0.7636 | | 0.5141 | 2.13 | 20000 | 0.5842 | 0.7648 | 0.7648 | | 0.4763 | 2.67 | 25000 | 0.5736 | 0.7637 | 0.7637 | | 0.4549 | 3.2 | 30000 | 0.6027 | 0.7593 | 0.7593 | | 0.4231 | 3.73 | 35000 | 0.6017 | 0.7552 | 0.7552 | | 0.3965 | 4.27 | 40000 | 0.6489 | 0.7551 | 0.7551 | | 0.3744 | 4.8 | 45000 | 0.6426 | 0.7534 | 0.7534 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
emre/wav2vec2-large-xlsr-53-demo-colab
emre
2022-01-24T10:54:03Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "robust-speech-event", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer - robust-speech-event datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-53-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-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3966 - Wer: 0.4834 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.1516 | 4.21 | 400 | 2.7673 | 1.0 | | 0.9134 | 8.42 | 800 | 0.4618 | 0.6418 | | 0.3273 | 12.63 | 1200 | 0.4188 | 0.5535 | | 0.2252 | 16.84 | 1600 | 0.4144 | 0.5232 | | 0.1692 | 21.05 | 2000 | 0.3995 | 0.5030 | | 0.1355 | 25.26 | 2400 | 0.4073 | 0.4920 | | 0.1172 | 29.47 | 2800 | 0.3966 | 0.4834 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
hfl/cino-base-v2
hfl
2022-01-24T10:34:45Z
124
5
transformers
[ "transformers", "pytorch", "tf", "xlm-roberta", "fill-mask", "zh", "bo", "kk", "ko", "mn", "ug", "yue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- language: - zh - bo - kk - ko - mn - ug - yue license: "apache-2.0" --- ## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型） Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding. We have seen rapid progress on building multilingual PLMs in recent year. However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems. To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as - Chinese，中文（zh） - Tibetan，藏语（bo） - Mongolian (Uighur form)，蒙语（mn） - Uyghur，维吾尔语（ug） - Kazakh (Arabic form)，哈萨克语（kk） - Korean，朝鲜语（ko） - Zhuang，壮语 - Cantonese，粤语（yue） Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM You may also interested in, Chinese MacBERT: https://github.com/ymcui/MacBERT Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA Chinese XLNet: https://github.com/ymcui/Chinese-XLNet Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer More resources by HFL: https://github.com/ymcui/HFL-Anthology
Vibharkchauhan/distilbert-base-uncased-finetuned-ner
Vibharkchauhan
2022-01-24T10:30:44Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9192622045504749 - name: Recall type: recall value: 0.9310884886452623 - name: F1 type: f1 value: 0.9251375534930251 - name: Accuracy type: accuracy value: 0.9823820039080496 --- <!-- 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-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0626 - Precision: 0.9193 - Recall: 0.9311 - F1: 0.9251 - Accuracy: 0.9824 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2393 | 1.0 | 878 | 0.0732 | 0.9052 | 0.9207 | 0.9129 | 0.9801 | | 0.0569 | 2.0 | 1756 | 0.0626 | 0.9193 | 0.9311 | 0.9251 | 0.9824 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-localParams
nntadotzip
2022-01-24T08:29:47Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlnet", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- license: mit tags: - generated_from_trainer model-index: - name: xlnet-base-cased-IUChatbot-ontologyDts-localParams 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. --> # xlnet-base-cased-IUChatbot-ontologyDts-localParams This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0238 ## 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: 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 | |:-------------:|:-----:|:----:|:---------------:| | 0.1172 | 1.0 | 1119 | 0.0657 | | 0.0564 | 2.0 | 2238 | 0.0237 | | 0.033 | 3.0 | 3357 | 0.0238 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
st1992/paraphrase-MiniLM-L12-tagalog-v2
st1992
2022-01-24T05:48:32Z
4
0
transformers
[ "transformers", "pytorch", "bert", "feature-extraction", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-03-02T23:29:05Z
# st1992/paraphrase-MiniLM-L12-tagalog-v2 paraphrase-MiniLM-L12-v2 finetuned on Tagalog language: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) : same as other sentence-transformer models ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('st1992/paraphrase-MiniLM-L12-tagalog-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) ```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 = ['hindi po', 'tulog na'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('st1992/paraphrase-MiniLM-L12-tagalog-v2') model = AutoModel.from_pretrained('st1992/paraphrase-MiniLM-L12-tagalog-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ```
guoqiang/WuDaoSailing
guoqiang
2022-01-24T05:39:39Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
# WudaoSailing WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models. ## Get Started ### Docker Image We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file [docs/docker/cuda102.dockerfile](docs/docker/cuda102.dcokerfile) or pull the pre-built images from Docker Hub and run with docker v19.03+ ```shell nvidia-docker run -id --hostname=V100 --network=host\ --ipc=host --shm-size=16gb --name=deepspeed-cuda \ -e NVIDIA_VISIBLE_DEVICES=0,1,2,3 \ -v /DATA/disk1/docker/containers/:/data deepspeed/cuda102:lastest ``` or replace `cuda102` with `cuda112`. ```shell docker build -f cuda102.dockerfile -t deepspeed/cuda102 . ``` ### Clone this repo ```shell git clone https://github.com/wangguojim/WudaoSailing.git cd WudaoSailing pip install -r requirements.txt ``` ## GLM We show some examples based on GLM model. ### finetuene We provide scripts for finetuning GLM on some downstream tasks. #### SuperGLUE - Download the [SuperGlue](https://super.gluebenchmark.com/tasks) data and check the experiment setup in [examples/glm/scripts/ds_finetune_superglue.sh](xamples/glm/scripts/ds_finetune_superglue.sh). Note that `DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH` need to be changed to your local path. You may also change the `batch-size` and `nproc_per_node` according to your available hardware. - Run the following script for text similarity finetune task (use the afqmc dataset as an example) ``` cd examples/glm/ bash scripts/ds_finetune_superglue.sh\ config/model_blocklm_large_chinese.sh\ config_tasks/task_afqmc.sh ``` - Run the following script for text classification finetune task (use the thunews and thunews dataset as an example) ``` cd examples/glm/ bash scripts/ds_finetune_superglue.sh\ config/model_blocklm_large_chinese.sh\ config_tasks/task_tnews.sh ``` - Run the following script for causal inference finetune task (use the COPA dataset as an example) ``` cd examples/glm/ bash scripts/ds_finetune_superglue.sh\ config/model_blocklm_large_chinese.sh\ config_tasks/task_copa.sh ``` - To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a `DataProcessor` in [examples/glm/tasks/superglue/dataset.py](examples/glm/tasks/superglue/dataset.py) for data loading and add a `PVP` in [examples/glm/tasks/superglue/pvp.py](examples/glm/tasks/superglue/pvp.py) for the cloze question. More details can be found [here](examples/glm/tasks/superglue/README.md). #### Blank Filling (Interactive) * Change `CHECKPOINT_PATH` to your local path. Run the following script ``` bash config/generate_block.sh\ config/model_blocklm_large_chinese.sh ``` ##### Example1 (Entity Prediction): Context: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。 GLM:拿破仑军队攻克米兰城 ##### Example2 (Sentence Prediction) Context: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。 GLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。 ##### Example3 (Long Text Generation) Context: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK] GLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙. ### Ptuning Run the following script to integrate p-tuning with GLM: ```shell cd algutils/ptuning/ bash finetune_zy.sh ``` ### Pretrain Run the following script to pre-train the GLM-Large model ```shell cd examples/glm/ bash scripts/ds_pretrain_nvidia.sh config/ds_block_large.sh ``` The script [examples/glm/config/ds_pretrain_nvidia.sh](examples/glm/config/ds_pretrain_nvidia.sh) launches the training program with DeepSpeed. You should change `NUM_WORKERS` and `NUM_GPUS_PER_WORKER` to the number of workers and the number of gpus per worker. Also change `HOST_FILE_PATH` to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found [here](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node). The file [examples/glm/config/ds_block_large.sh](examples/glm/config/ds_block_large.sh) defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, `--train-data` can be multiple keywords defined in `NAMED_CORPORA` in [data_utils/corpora.py](data_utils/corpora.py). The hyperparameters of the optimizer are defined in the corresponding json file under `config`. The semantics of the json file can be found [here](https://www.deepspeed.ai/docs/config-json). ## Bert We show some examples based on GLM model. ### Pretrain Run the following script to pre-train the Bert model ```shell cd examples/bert/ python quick_start.py ``` ## CogView ### Pretrain Run the following script to pre-train the cogview model ```shell cd examples/cogview/ bash config/pretrain_multiple_nodes.sh ``` ### inference Run the following script to test the ability of text2image ```shell cd examples/cogview/ bash config/text2image_cogview.sh ```
haji2438/bertweet-base-SNS_BRANDS_50k
haji2438
2022-01-24T03:51:35Z
8
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- tags: - generated_from_trainer model-index: - name: bertweet-base-SNS_BRANDS_50k 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. --> # bertweet-base-SNS_BRANDS_50k This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0490 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0787 | 1.0 | 1465 | 0.0751 | | 0.0662 | 2.0 | 2930 | 0.0628 | | 0.053 | 3.0 | 4395 | 0.0531 | | 0.0452 | 4.0 | 5860 | 0.0490 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
public-data/bizarre-pose-estimator-models
public-data
2022-01-24T03:35:52Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
# bizarre-pose-estimator - Repo: https://github.com/ShuhongChen/bizarre-pose-estimator - https://drive.google.com/drive/folders/11bw47Vy-RPKjgd6yF0RzcXALvp7zB_wt
jiobiala24/wav2vec2-base-checkpoint-8
jiobiala24
2022-01-24T01:26:07Z
4
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
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-checkpoint-8 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-checkpoint-8 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-7.1](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-7.1) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9561 - Wer: 0.3271 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3117 | 1.59 | 1000 | 0.5514 | 0.3451 | | 0.2509 | 3.19 | 2000 | 0.5912 | 0.3328 | | 0.1918 | 4.78 | 3000 | 0.6103 | 0.3346 | | 0.1612 | 6.38 | 4000 | 0.6469 | 0.3377 | | 0.1388 | 7.97 | 5000 | 0.6597 | 0.3391 | | 0.121 | 9.57 | 6000 | 0.6911 | 0.3472 | | 0.1096 | 11.16 | 7000 | 0.7300 | 0.3457 | | 0.0959 | 12.76 | 8000 | 0.7660 | 0.3400 | | 0.0882 | 14.35 | 9000 | 0.8316 | 0.3394 | | 0.0816 | 15.95 | 10000 | 0.8042 | 0.3357 | | 0.0739 | 17.54 | 11000 | 0.8087 | 0.3346 | | 0.0717 | 19.14 | 12000 | 0.8590 | 0.3353 | | 0.066 | 20.73 | 13000 | 0.8750 | 0.3336 | | 0.0629 | 22.33 | 14000 | 0.8759 | 0.3333 | | 0.0568 | 23.92 | 15000 | 0.8963 | 0.3321 | | 0.0535 | 25.52 | 16000 | 0.9391 | 0.3323 | | 0.0509 | 27.11 | 17000 | 0.9279 | 0.3296 | | 0.0498 | 28.71 | 18000 | 0.9561 | 0.3271 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
anas-awadalla/bert-medium-finetuned-squad
anas-awadalla
2022-01-24T01:10:28Z
5
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
Results: {'exact_match': 76.82119205298014, 'f1': 84.69734248389383}
public-data/Yet-Another-Anime-Segmenter
public-data
2022-01-24T00:00:14Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
# Yet-Another-Anime-Segmenter - Repo: https://github.com/zymk9/Yet-Another-Anime-Segmenter - https://drive.google.com/file/d/1-wFdQ4jwSTeJ7wGD3YKNJdcpSS5Ho8c9/view?usp=sharing - https://raw.githubusercontent.com/zymk9/Yet-Another-Anime-Segmenter/main/configs/SOLOv2.yaml
mattchurgin/xls-r-eng
mattchurgin
2022-01-23T17:31:10Z
6
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "ab", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: - ab license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' 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. --> # This model is a fine-tuned version of [patrickvonplaten/wav2vec2_tiny_random_robust](https://huggingface.co/patrickvonplaten/wav2vec2_tiny_random_robust) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 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: 0.0003 - 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: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1 - Datasets 1.18.1.dev0 - Tokenizers 0.11.0
shivam/wav2vec2-xls-r-300m-hindi
shivam
2022-01-23T16:37:08Z
4
1
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "hi", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: - hi license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' 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. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 1.4031 - Wer: 0.6827 ## 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: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.3156 | 3.4 | 500 | 4.5583 | 1.0 | | 3.3329 | 6.8 | 1000 | 3.4274 | 1.0001 | | 2.1275 | 10.2 | 1500 | 1.7221 | 0.8763 | | 1.5737 | 13.6 | 2000 | 1.4188 | 0.8143 | | 1.3835 | 17.01 | 2500 | 1.2251 | 0.7447 | | 1.3247 | 20.41 | 3000 | 1.2827 | 0.7394 | | 1.231 | 23.81 | 3500 | 1.2216 | 0.7074 | | 1.1819 | 27.21 | 4000 | 1.2210 | 0.6863 | | 1.1546 | 30.61 | 4500 | 1.3233 | 0.7308 | | 1.0902 | 34.01 | 5000 | 1.3251 | 0.7010 | | 1.0749 | 37.41 | 5500 | 1.3274 | 0.7235 | | 1.0412 | 40.81 | 6000 | 1.2942 | 0.6856 | | 1.0064 | 44.22 | 6500 | 1.2581 | 0.6732 | | 1.0006 | 47.62 | 7000 | 1.2767 | 0.6885 | | 0.9518 | 51.02 | 7500 | 1.2966 | 0.6925 | | 0.9514 | 54.42 | 8000 | 1.2981 | 0.7067 | | 0.9241 | 57.82 | 8500 | 1.3835 | 0.7124 | | 0.9059 | 61.22 | 9000 | 1.3318 | 0.7083 | | 0.8906 | 64.62 | 9500 | 1.3640 | 0.6962 | | 0.8468 | 68.03 | 10000 | 1.4727 | 0.6982 | | 0.8631 | 71.43 | 10500 | 1.3401 | 0.6809 | | 0.8154 | 74.83 | 11000 | 1.4124 | 0.6955 | | 0.7953 | 78.23 | 11500 | 1.4245 | 0.6950 | | 0.818 | 81.63 | 12000 | 1.3944 | 0.6995 | | 0.7772 | 85.03 | 12500 | 1.3735 | 0.6785 | | 0.7857 | 88.43 | 13000 | 1.3696 | 0.6808 | | 0.7705 | 91.84 | 13500 | 1.4101 | 0.6870 | | 0.7537 | 95.24 | 14000 | 1.4178 | 0.6832 | | 0.7734 | 98.64 | 14500 | 1.4027 | 0.6831 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu113 - Datasets 1.18.1.dev0 - Tokenizers 0.11.0
Emanuel/roebrta-base-val-test
Emanuel
2022-01-23T15:12:04Z
6
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:04Z
--- license: mit tags: - generated_from_trainer model-index: - name: language-modeling 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. --> # language-modeling This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4229 ## 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 - distributed_type: tpu - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.8.1+cu102 - Datasets 1.13.3 - Tokenizers 0.10.3
artemis13fowl/distilbert-base-uncased-finetuned-imdb
artemis13fowl
2022-01-23T14:10:31Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-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. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4725 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4897 | | 2.5756 | 2.0 | 314 | 2.4230 | | 2.5395 | 3.0 | 471 | 2.4358 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
dandelin/vilt-b32-finetuned-nlvr2
dandelin
2022-01-23T09:43:30Z
673
2
transformers
[ "transformers", "pytorch", "vilt", "arxiv:2102.03334", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- license: apache-2.0 --- # Vision-and-Language Transformer (ViLT), fine-tuned on NLVR2 Vision-and-Language Transformer (ViLT) model fine-tuned on [NLVR2](https://lil.nlp.cornell.edu/nlvr/). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Intended uses & limitations You can use the model to determine whether a sentence is true or false given 2 images. ### How to use Here is how to use the model in PyTorch: ``` from transformers import ViltProcessor, ViltForImagesAndTextClassification import requests from PIL import Image image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw) image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_1.jpg", stream=True).raw) text = "The left image contains twice the number of dogs as the right image." processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2") model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2") # prepare inputs encoding = processor([image1, image2], text, return_tensors="pt") # forward pass outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0)) logits = outputs.logits idx = logits.argmax(-1).item() print("Predicted answer:", model.config.id2label[idx]) ``` ## Training data (to do) ## Training procedure ### Preprocessing (to do) ### Pretraining (to do) ## Evaluation results (to do) ### BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} } ```
wesam266/wav2vec2-large-xlsr-53_english
wesam266
2022-01-23T02:40:28Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53_english 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-xlsr-53_english This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2620 - Wer: 0.1916 ## 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: 0.0005 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0506 | 0.12 | 250 | 3.0206 | 0.9999 | | 1.4381 | 0.25 | 500 | 1.0267 | 0.6323 | | 1.0903 | 0.37 | 750 | 0.5841 | 0.3704 | | 1.0384 | 0.5 | 1000 | 0.5156 | 0.3348 | | 0.9658 | 0.62 | 1250 | 0.4721 | 0.3221 | | 0.9184 | 0.74 | 1500 | 0.4301 | 0.3213 | | 0.8939 | 0.87 | 1750 | 0.4188 | 0.2884 | | 0.9051 | 0.99 | 2000 | 0.3852 | 0.2807 | | 0.563 | 1.12 | 2250 | 0.3752 | 0.2804 | | 0.6122 | 1.24 | 2500 | 0.3745 | 0.2732 | | 0.6213 | 1.36 | 2750 | 0.3671 | 0.2575 | | 0.5839 | 1.49 | 3000 | 0.3560 | 0.2578 | | 0.615 | 1.61 | 3250 | 0.3555 | 0.2536 | | 0.5557 | 1.74 | 3500 | 0.3511 | 0.2485 | | 0.5497 | 1.86 | 3750 | 0.3364 | 0.2425 | | 0.5412 | 1.98 | 4000 | 0.3253 | 0.2418 | | 0.2834 | 2.11 | 4250 | 0.3293 | 0.2322 | | 0.2723 | 2.23 | 4500 | 0.3157 | 0.2322 | | 0.2713 | 2.35 | 4750 | 0.3148 | 0.2304 | | 0.2878 | 2.48 | 5000 | 0.3143 | 0.2286 | | 0.2776 | 2.6 | 5250 | 0.3122 | 0.2250 | | 0.2553 | 2.73 | 5500 | 0.3003 | 0.2234 | | 0.278 | 2.85 | 5750 | 0.2973 | 0.2198 | | 0.2445 | 2.97 | 6000 | 0.2938 | 0.2180 | | 0.4361 | 3.1 | 6250 | 0.2914 | 0.2132 | | 0.3979 | 3.22 | 6500 | 0.2916 | 0.2125 | | 0.4221 | 3.35 | 6750 | 0.2879 | 0.2113 | | 0.4051 | 3.47 | 7000 | 0.2819 | 0.2100 | | 0.4218 | 3.59 | 7250 | 0.2812 | 0.2072 | | 0.4201 | 3.72 | 7500 | 0.2772 | 0.2055 | | 0.3515 | 3.84 | 7750 | 0.2747 | 0.2031 | | 0.4021 | 3.97 | 8000 | 0.2702 | 0.2018 | | 0.4304 | 4.09 | 8250 | 0.2721 | 0.2007 | | 0.3923 | 4.21 | 8500 | 0.2689 | 0.1991 | | 0.3824 | 4.34 | 8750 | 0.2692 | 0.1980 | | 0.3743 | 4.46 | 9000 | 0.2718 | 0.1950 | | 0.3771 | 4.59 | 9250 | 0.2653 | 0.1950 | | 0.4048 | 4.71 | 9500 | 0.2649 | 0.1934 | | 0.3539 | 4.83 | 9750 | 0.2638 | 0.1919 | | 0.3498 | 4.96 | 10000 | 0.2620 | 0.1916 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.1+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3
ylh1013/ja_chatbot
ylh1013
2022-01-23T02:24:03Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- language: - finetuned_from license: mit tags: - generated_from_trainer model-index: - name: ja_chatbot 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. --> # ja_chatbot This model is a fine-tuned version of [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium) on an unknown 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: 5e-05 - train_batch_size: 48 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu102 - Tokenizers 0.10.3
Pinwheel/wav2vec2-base-timit-demo-colab
Pinwheel
2022-01-22T15:04:16Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:04Z
--- 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. It achieves the following results on the evaluation set: - Loss: 0.4812 - Wer: 0.3557 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4668 | 4.0 | 500 | 1.3753 | 0.9895 | | 0.6126 | 8.0 | 1000 | 0.4809 | 0.4350 | | 0.2281 | 12.0 | 1500 | 0.4407 | 0.4033 | | 0.1355 | 16.0 | 2000 | 0.4590 | 0.3765 | | 0.0923 | 20.0 | 2500 | 0.4754 | 0.3707 | | 0.0654 | 24.0 | 3000 | 0.4719 | 0.3557 | | 0.0489 | 28.0 | 3500 | 0.4812 | 0.3557 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
facebook/xm_transformer_600m-en_es-multi_domain
facebook
2022-01-21T19:01:24Z
2
1
fairseq
[ "fairseq", "audio", "audio-to-audio", "speech-to-speech-translation", "dataset:must_c", "dataset:europarl_st", "dataset:voxpopuli", "arxiv:2010.05171", "region:us" ]
audio-to-audio
2022-03-02T23:29:05Z
--- library_name: fairseq task: audio-to-audio tags: - fairseq - audio - audio-to-audio - speech-to-speech-translation language: en-es datasets: - must_c - europarl_st - voxpopuli widget: - example_title: Common Voice sample 1 src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/main/common_voice_en_18295850.mp3 --- # xm_transformer_600m-en_es-multi_domain [W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)): - English-Spanish - Trained on MuST-C, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix - Speech synthesis with [facebook/tts_transformer-es-css10](https://huggingface.co/facebook/tts_transformer-es-css10) ## Usage ```python from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub from fairseq.models.text_to_speech.hub_interface import S2THubInterface from fairseq.models.text_to_speech.hub_interface import TTSHubInterface import IPython.display as ipd import torchaudio models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "facebook/xm_transformer_600m-en_es-multi_domain", arg_overrides={"config_yaml": "config.yaml"}, ) model = models[0] generator = task.build_generator(model, cfg) # requires 16000Hz mono channel audio audio, _ = torchaudio.load("/path/to/an/audio/file") sample = S2THubInterface.get_model_input(task, audio) text = S2THubInterface.get_prediction(task, model, generator, sample) # speech synthesis tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub( f"facebook/tts_transformer-es-css10", arg_overrides={"vocoder": "griffin_lim", "fp16": False}, ) tts_model = tts_models[0] TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg) tts_generator = tts_task.build_generator([tts_model], tts_cfg) tts_sample = TTSHubInterface.get_model_input(tts_task, text) wav, sr = TTSHubInterface.get_prediction( tts_task, tts_model, tts_generator, tts_sample ) ipd.Audio(wav, rate=rate) ``` ## Citation ```bibtex @inproceedings{li-etal-2021-multilingual, title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models", author = "Li, Xian and Wang, Changhan and Tang, Yun and Tran, Chau and Tang, Yuqing and Pino, Juan and Baevski, Alexei and Conneau, Alexis and Auli, Michael", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.68", doi = "10.18653/v1/2021.acl-long.68", pages = "827--838", } @inproceedings{wang-etal-2020-fairseq, title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq", author = "Wang, Changhan and Tang, Yun and Ma, Xutai and Wu, Anne and Okhonko, Dmytro and Pino, Juan", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.aacl-demo.6", pages = "33--39", } ```
deepparag/DumBot
deepparag
2022-01-21T15:40:27Z
148
2
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-03-02T23:29:05Z
--- thumbnail: https://cdn.discordapp.com/app-icons/870239976690970625/c02cae78ae105f07969cfd8f8ea3d0a0.png tags: - conversational license: mit --- # THIS AI IS OUTDATED. See [Aeona](https://huggingface.co/deepparag/Aeona) An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small). Trained on: https://www.kaggle.com/Cornell-University/movie-dialog-corpus https://www.kaggle.com/jef1056/discord-data [Live Demo](https://dumbot-331213.uc.r.appspot.com/) Example: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("deepparag/DumBot") model = AutoModelWithLMHead.from_pretrained("deepparag/DumBot") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=4, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("DumBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```
infinitejoy/Wav2Vec2-Large-XLSR-53-Odia
infinitejoy
2022-01-21T13:19:09Z
4
0
transformers
[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "or", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: or datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Joydeep Bhattacharjee XLSR Wav2Vec2 Large 53 Odia results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice as type: common_voice args: or metrics: - name: Test WER type: wer value: 55.07 --- # Wav2Vec2-Large-XLSR-53-Odia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Odia using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "or", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Odia") model = Wav2Vec2ForCTC.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Odia") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Odia test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "or", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Odia") model = Wav2Vec2ForCTC.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Odia") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\।\–]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub('’ ',' ',batch["sentence"]) batch["sentence"] = re.sub(' ‘',' ',batch["sentence"]) batch["sentence"] = re.sub('’|‘','\'',batch["sentence"]) batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 55.07 % ## Training The Common Voice `train` and `validation` datasets were used for training.
alistvt/bert-base-uncased-pretrained-mlm-coqa-stories
alistvt
2022-01-21T13:17:32Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- tags: - generated_from_trainer model-index: - name: bert-base-uncased-pretrained-mlm-coqa-stories 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-pretrained-mlm-coqa-stories This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8310 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0573 | 1.0 | 2479 | 1.8805 | | 1.9517 | 2.0 | 4958 | 1.8377 | | 1.9048 | 3.0 | 7437 | 1.8310 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387
MadhurJindalWorkMail
2022-01-21T07:05:45Z
3
1
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autonlp", "en", "dataset:MadhurJindalWorkMail/autonlp-data-Gibb-Detect", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - MadhurJindalWorkMail/autonlp-data-Gibb-Detect co2_eq_emissions: 70.95647633212745 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 515314387 - CO2 Emissions (in grams): 70.95647633212745 ## Validation Metrics - Loss: 0.08077705651521683 - Accuracy: 0.9760103738923709 - Macro F1: 0.9728412857204902 - Micro F1: 0.9760103738923709 - Weighted F1: 0.9759907151741426 - Macro Precision: 0.9736622407675567 - Micro Precision: 0.9760103738923709 - Weighted Precision: 0.97673611876005 - Macro Recall: 0.9728978421381711 - Micro Recall: 0.9760103738923709 - Weighted Recall: 0.9760103738923709 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp
espnet
2022-01-21T04:15:13Z
8
2
espnet
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- tags: - espnet - audio - automatic-speech-recognition language: en datasets: - librispeech license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp` This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout b0ff60946ada6753af79423a2e6063984bec2926 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp ``` ## ASR config <details><summary>expand</summary> ``` ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp
espnet
2022-01-21T04:09:13Z
4
0
espnet
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- tags: - espnet - audio - automatic-speech-recognition language: en datasets: - librispeech license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp` This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout b0ff60946ada6753af79423a2e6063984bec2926 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp ``` ## ASR config <details><summary>expand</summary> ``` ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Gigworks/ASR_zh_espnet2
Gigworks
2022-01-21T02:58:59Z
0
1
null
[ "region:us" ]
null
2022-03-02T23:29:04Z
<b>Speech-To-Text Chinese Model</b> <br/><br/> Reference: <br/> Model - https://huggingface.co/espnet/pengcheng_guo_wenetspeech_asr_train_asr_raw_zh_char <br/> Code - https://huggingface.co/spaces/akhaliq/espnet2_asr/blob/main/app.py
guoqiang/glm
guoqiang
2022-01-21T01:21:46Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
# WudaoSailing WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models. ## Get Started ### Docker Image We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file [docs/docker/cuda102.dockerfile](docs/docker/cuda102.dcokerfile) or pull the pre-built images from Docker Hub and run with docker v19.03+ ```shell nvidia-docker run -id --hostname=V100 --network=host\ --ipc=host --shm-size=16gb --name=deepspeed-cuda \ -e NVIDIA_VISIBLE_DEVICES=0,1,2,3 \ -v /DATA/disk1/docker/containers/:/data deepspeed/cuda102:lastest ``` or replace `cuda102` with `cuda112`. ```shell docker build -f cuda102.dockerfile -t deepspeed/cuda102 . ``` ### Clone this repo ```shell git clone https://github.com/wangguojim/WudaoSailing.git cd WudaoSailing pip install -r requirements.txt ``` ## GLM We show some examples based on GLM model. ### finetuene We provide scripts for finetuning GLM on some downstream tasks. #### SuperGLUE - Download the [SuperGlue](https://super.gluebenchmark.com/tasks) data and check the experiment setup in [examples/glm/scripts/ds_finetune_superglue.sh](xamples/glm/scripts/ds_finetune_superglue.sh). Note that `DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH` need to be changed to your local path. You may also change the `batch-size` and `nproc_per_node` according to your available hardware. - Run the following script for text similarity finetune task (use the afqmc dataset as an example) ``` cd examples/glm/ bash scripts/ds_finetune_superglue.sh\ config/model_blocklm_large_chinese.sh\ config_tasks/task_afqmc.sh ``` - Run the following script for text classification finetune task (use the thunews and thunews dataset as an example) ``` cd examples/glm/ bash scripts/ds_finetune_superglue.sh\ config/model_blocklm_large_chinese.sh\ config_tasks/task_tnews.sh ``` - Run the following script for causal inference finetune task (use the COPA dataset as an example) ``` cd examples/glm/ bash scripts/ds_finetune_superglue.sh\ config/model_blocklm_large_chinese.sh\ config_tasks/task_copa.sh ``` - To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a `DataProcessor` in [examples/glm/tasks/superglue/dataset.py](examples/glm/tasks/superglue/dataset.py) for data loading and add a `PVP` in [examples/glm/tasks/superglue/pvp.py](examples/glm/tasks/superglue/pvp.py) for the cloze question. More details can be found [here](examples/glm/tasks/superglue/README.md). #### Blank Filling (Interactive) * Change `CHECKPOINT_PATH` to your local path. Run the following script ``` bash config/generate_block.sh\ config/model_blocklm_large_chinese.sh ``` ##### Example1 (Entity Prediction): Context: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。 GLM:拿破仑军队攻克米兰城 ##### Example2 (Sentence Prediction) Context: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。 GLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。 ##### Example3 (Long Text Generation) Context: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK] GLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙. ### Ptuning Run the following script to integrate p-tuning with GLM: ```shell cd algutils/ptuning/ bash finetune_zy.sh ``` ### Pretrain Run the following script to pre-train the GLM-Large model ```shell cd examples/glm/ bash scripts/ds_pretrain_nvidia.sh config/ds_block_large.sh ``` The script [examples/glm/config/ds_pretrain_nvidia.sh](examples/glm/config/ds_pretrain_nvidia.sh) launches the training program with DeepSpeed. You should change `NUM_WORKERS` and `NUM_GPUS_PER_WORKER` to the number of workers and the number of gpus per worker. Also change `HOST_FILE_PATH` to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found [here](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node). The file [examples/glm/config/ds_block_large.sh](examples/glm/config/ds_block_large.sh) defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, `--train-data` can be multiple keywords defined in `NAMED_CORPORA` in [data_utils/corpora.py](data_utils/corpora.py). The hyperparameters of the optimizer are defined in the corresponding json file under `config`. The semantics of the json file can be found [here](https://www.deepspeed.ai/docs/config-json). ## Bert We show some examples based on GLM model. ### Pretrain Run the following script to pre-train the Bert model ```shell cd examples/bert/ python quick_start.py ``` ## CogView ### Pretrain Run the following script to pre-train the cogview model ```shell cd examples/cogview/ bash config/pretrain_multiple_nodes.sh ``` ### inference Run the following script to test the ability of text2image ```shell cd examples/cogview/ bash config/text2image_cogview.sh ```
Gianpe/en_textcat_emotion_umberto
Gianpe
2022-01-20T21:45:19Z
1
0
spacy
[ "spacy", "text-classification", "en", "region:us" ]
text-classification
2022-03-02T23:29:04Z
--- tags: - spacy - text-classification language: - en model-index: - name: en_textcat_emotion_umberto results: [] ---
anuragshas/wav2vec2-large-xls-r-300m-hi
anuragshas
2022-01-20T20:38:42Z
4
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
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hi 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-hi 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. It achieves the following results on the evaluation set: - Loss: 2.4156 - Wer: 0.7181 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.7703 | 2.72 | 400 | 2.2274 | 0.9259 | | 0.6515 | 5.44 | 800 | 1.5812 | 0.7581 | | 0.339 | 8.16 | 1200 | 2.0590 | 0.7825 | | 0.2262 | 10.88 | 1600 | 2.0324 | 0.7603 | | 0.1665 | 13.6 | 2000 | 2.1396 | 0.7481 | | 0.1311 | 16.33 | 2400 | 2.2090 | 0.7379 | | 0.1079 | 19.05 | 2800 | 2.3907 | 0.7612 | | 0.0927 | 21.77 | 3200 | 2.5294 | 0.7478 | | 0.0748 | 24.49 | 3600 | 2.5024 | 0.7452 | | 0.0644 | 27.21 | 4000 | 2.4715 | 0.7307 | | 0.0569 | 29.93 | 4400 | 2.4156 | 0.7181 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
muellerzr/fastai-pets-resnet-34
muellerzr
2022-01-20T19:01:14Z
0
1
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
# The fastai models - PETS This model is based on Lesson 1 of [fastai](https://course.fast.ai) and of [Walk with fastai](https://walkwithfastai.com/Pets) ## Dataset Used This model was created with the [Oxford Pets](https://docs.fast.ai/data.external.html#Image-Classification-datasets) dataset in the fastai framework ## Model Training The model was trained as a binary classifier, for cats or dogs ## How to use: First, ensure that `huggingface_hub` is installed: ```bash pip(3) install huggingface_hub ``` Next, download this model repo: ```python from huggingface_hub import snapshot_download snapshot_download(repo_id="muellerzr/fastai-pets-resnet-34") ``` Then install the correct fastai version: ```bash cd fastai-pets-resnet34 pip(3) install -r requirements.txt ``` **NOTE: This is extremely important, as fastai versions are aggressively pinned based on training environment** And finally load in the fastai `Learner` and predict ```python from fastai.learner import load_learner learn = load_learner('model.pth') pred = learn.predict('myImage.jpg') ``` Versions of model used were taken with [dependency_checker](https://muellerzr.github.io/dependency_checker)
espnet/akreal_swbd_da_hubert_conformer
espnet
2022-01-20T18:57:49Z
2
0
espnet
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:swbd_da", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- tags: - espnet - audio - automatic-speech-recognition language: en datasets: - swbd_da license: cc-by-4.0 --- ## ESPnet2 ASR model ### `akreal/espnet2_swbd_da_hubert_conformer` This model was trained by Pavel Denisov using swbd_da recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 08c6efbc6299c972301236625f9abafe087c9f9c pip install -e . cd egs2/swbd_da/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/akreal_swbd_da_hubert_conformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Thu Jan 20 19:31:21 CET 2022` - python version: `3.8.12 (default, Aug 30 2021, 00:00:00) [GCC 11.2.1 20210728 (Red Hat 11.2.1-1)]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.10.1+cu113` - Git hash: `08c6efbc6299c972301236625f9abafe087c9f9c` - Commit date: `Tue Jan 4 13:40:33 2022 +0100` ## asr_train_asr_raw_en_word_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.loss.ave/test_context3|2379|2379|66.3|33.7|0.0|0.0|33.7|33.7| |decode_asr_asr_model_valid.loss.ave/valid_context3|8116|8116|69.5|30.5|0.0|0.0|30.5|30.5| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.loss.ave/test_context3|2379|19440|76.1|17.7|6.2|8.1|32.0|33.7| |decode_asr_asr_model_valid.loss.ave/valid_context3|8116|66353|79.5|16.1|4.4|8.0|28.5|30.5| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_hubert_context3.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_hubert_context3_raw_en_word_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 35 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 7 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 4000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_context3_raw_en_word_sp/train/speech_shape - exp/asr_stats_context3_raw_en_word_sp/train/text_shape.word valid_shape_file: - exp/asr_stats_context3_raw_en_word_sp/valid/speech_shape - exp/asr_stats_context3_raw_en_word_sp/valid/text_shape.word batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_context3_sp/wav.scp - speech - sound - - dump/raw/train_context3_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/valid_context3/wav.scp - speech - sound - - dump/raw/valid_context3/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0001 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - statement - backchannel - opinion - abandon - agree - yn_q - apprec - 'yes' - uninterp - close - wh_q - acknowledge - 'no' - yn_decl_q - hedge - backchannel_q - sum - quote - affirm - other - directive - repeat - open_q - completion - rhet_q - hold - reject - answer - neg - ans_dispref - repeat_q - open - or - commit - maybe - decl_q - third_pty - self_talk - thank - apology - tag_q - downplay - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.0 extract_feats_in_collect_stats: false use_preprocessor: true token_type: word bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: s3prl frontend_conf: frontend_conf: upstream: hubert_large_ll60k download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.5a1 distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
ilevs/opus-mt-en-ru-finetuned-en-to-ru
ilevs
2022-01-20T18:18:30Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-en-ru-finetuned-en-to-ru 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. --> # opus-mt-en-ru-finetuned-en-to-ru This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7682 - Bleu: 14.6112 - Gen Len: 7.202 ## 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 2.3198 | 1.0 | 4956 | 2.1261 | 9.5339 | 6.7709 | | 1.9732 | 2.0 | 9912 | 1.9639 | 10.4715 | 7.1254 | | 1.7127 | 3.0 | 14868 | 1.8780 | 11.6128 | 7.1106 | | 1.5614 | 4.0 | 19824 | 1.8367 | 12.8389 | 7.0468 | | 1.4276 | 5.0 | 24780 | 1.8040 | 13.7423 | 7.0403 | | 1.3096 | 6.0 | 29736 | 1.7820 | 14.1469 | 7.0555 | | 1.2381 | 7.0 | 34692 | 1.7761 | 13.9987 | 7.2225 | | 1.1784 | 8.0 | 39648 | 1.7725 | 14.4675 | 7.1799 | | 1.1376 | 9.0 | 44604 | 1.7692 | 14.4937 | 7.1957 | | 1.0862 | 10.0 | 49560 | 1.7682 | 14.6112 | 7.202 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-BertPretrainedTokenizerFast
nntadotzip
2022-01-20T18:06:05Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlnet", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- license: mit tags: - generated_from_trainer model-index: - name: xlnet-base-cased-IUChatbot-ontologyDts-BertPretrainedTokenizerFast 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. --> # xlnet-base-cased-IUChatbot-ontologyDts-BertPretrainedTokenizerFast This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3489 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 382 | 0.4695 | | 0.5633 | 2.0 | 764 | 0.3361 | | 0.3533 | 3.0 | 1146 | 0.3489 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Rocketknight1/distilroberta-base-finetuned-wikitext2
Rocketknight1
2022-01-20T17:54:46Z
22
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:04Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilroberta-base-finetuned-wikitext2 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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - TensorFlow 2.8.0-rc0 - Datasets 1.17.0 - Tokenizers 0.11.0
nntadotzip/bert-base-cased-IUChatbot-ontologyDts
nntadotzip
2022-01-20T16:21:21Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-IUChatbot-ontologyDts 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-cased-IUChatbot-ontologyDts This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2446 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 382 | 0.2686 | | 0.3946 | 2.0 | 764 | 0.2535 | | 0.2577 | 3.0 | 1146 | 0.2446 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
radhakri119/wav2vec2-base-timit-demo-colab
radhakri119
2022-01-20T16:09:09Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- 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. It achieves the following results on the evaluation set: - Loss: 0.4780 - Wer: 0.3403 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5299 | 4.0 | 500 | 1.5195 | 0.9991 | | 0.6229 | 8.0 | 1000 | 0.4447 | 0.4282 | | 0.2136 | 12.0 | 1500 | 0.4154 | 0.3764 | | 0.1196 | 16.0 | 2000 | 0.4394 | 0.3597 | | 0.0834 | 20.0 | 2500 | 0.4891 | 0.3619 | | 0.0591 | 24.0 | 3000 | 0.4535 | 0.3439 | | 0.0448 | 28.0 | 3500 | 0.4780 | 0.3403 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
ml6team/distilbart-tos-summarizer-tosdr
ml6team
2022-01-20T15:21:41Z
22
15
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "summarization", "t&c", "tos", "distilbart", "distilbart-6-6", "en", "dataset:tosdr", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2022-03-02T23:29:05Z
--- language: - en tags: - summarization - t&c - tos - distilbart - distilbart-6-6 datasets: - tosdr metrics: - rouge1 - rouge2 - rougel inference: parameters: min_length: 5 max_length: 512 do_sample: False widget: - text: "In addition, certain portions of the Web Site may be subject to additional terms of use that we make available for your review or otherwise link to that portion of the Web Site to which such additional terms apply. By using such portions, or any part thereof, you agree to be bound by the additional terms of use applicable to such portions. Age Restrictions The Web Site may be accessed and used only by individuals who can form legally binding contracts under applicable laws, who are at least 18 years of age or the age of majority in their state or territory of residence (if higher than 18), and who are not barred from using the Web Site under applicable laws. Our Technology may not be copied, modified, reproduced, republished, posted, transmitted, sold, offered for sale, or redistributed in any way without our prior written permission and the prior written permission of our applicable licensors. Nothing in these Site Terms of Use grants you any right to receive delivery of a copy of Our Technology or to obtain access to Our Technology except as generally and ordinarily permitted through the Web Site according to these Site Terms of Use. Furthermore, nothing in these Site Terms of Use will be deemed to grant you, by implication, estoppel or otherwise, a license to Our Technology. Certain of the names, logos, and other materials displayed via the Web site constitute trademarks, tradenames, service marks or logos (“Marks”) of us or other entities. You are not authorized to use any such Marks. Ownership of all such Marks and the goodwill associated therewith remains with us or those other entities. Any use of third party software provided in connection with the Web Site will be governed by such third parties’ licenses and not by these Site Terms of Use. Information on this Web Site may contain technical inaccuracies or typographical errors. Lenovo provides no assurances that any reported problems may be resolved with the use of any information that Lenovo provides." --- # T&C Summarization Model T&C Summarization Model based on [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6), This abstractive summarization model is a part of a bigger end-to-end T&C summarizer pipeline which is preceded by LSA (Latent Semantic Analysis) extractive summarization. The extractive summarization shortens the T&C to be further summarized by this model. ## Finetuning Corpus We collaborated with [TOSDR](https://tosdr.org/) to work with their data, and the model is finetuned accordingly. The article and summarization text is reduced via extractive summarization before it is finetuned to the model. ## Contact Us https://ml6.eu/ . This abstractive model finetuning is the continuation of the Christmas Project 2021 done in ML6: https://bit.ly/XmasProjects . ## Load Finetuned Model ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr") model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr") ``` ## Code Sample This sample requires [sumy](https://pypi.org/project/sumy/), the LSA Extractive Summarization library, as additional package to run. ``` import re import nltk nltk.download('punkt') from sumy.parsers.plaintext import PlaintextParser from sumy.nlp.tokenizers import Tokenizer from sumy.nlp.stemmers import Stemmer from sumy.summarizers.lsa import LsaSummarizer from transformers import AutoTokenizer, AutoModelForSeq2SeqLM LANGUAGE = "english" EXTRACTED_ARTICLE_SENTENCES_LEN = 12 stemmer = Stemmer(LANGUAGE) lsa_summarizer = LsaSummarizer(stemmer) tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr") model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr") def get_extractive_summary(text, sentences_count): parser = PlaintextParser.from_string(text, Tokenizer(LANGUAGE)) summarized_info = lsa_summarizer(parser.document, sentences_count) summarized_info = [element._text for element in summarized_info] return ' '.join(summarized_info) def get_summary(dict_summarizer_model, dict_tokenizer, text_content): text_content = get_extractive_summary(text_content, EXTRACTED_ARTICLE_SENTENCES_LEN) tokenizer = dict_tokenizer['tokenizer'] model = dict_summarizer_model['model'] inputs = tokenizer(text_content, max_length=dict_tokenizer['max_length'], truncation=True, return_tensors="pt") outputs = model.generate( inputs["input_ids"], max_length=dict_summarizer_model['max_length'], min_length=dict_summarizer_model['min_length'], ) summarized_text = tokenizer.decode(outputs[0]) match = re.search(r"<s>(.*)</s>", summarized_text) if match is not None: summarized_text = match.group(1) return summarized_text.replace('<s>', '').replace('</s>', '') test_tos = """ In addition, certain portions of the Web Site may be subject to additional terms of use that we make available for your review or otherwise link to that portion of the Web Site to which such additional terms apply. By using such portions, or any part thereof, you agree to be bound by the additional terms of use applicable to such portions. Age Restrictions The Web Site may be accessed and used only by individuals who can form legally binding contracts under applicable laws, who are at least 18 years of age or the age of majority in their state or territory of residence (if higher than 18), and who are not barred from using the Web Site under applicable laws. Our Technology may not be copied, modified, reproduced, republished, posted, transmitted, sold, offered for sale, or redistributed in any way without our prior written permission and the prior written permission of our applicable licensors. Nothing in these Site Terms of Use grants you any right to receive delivery of a copy of Our Technology or to obtain access to Our Technology except as generally and ordinarily permitted through the Web Site according to these Site Terms of Use. Furthermore, nothing in these Site Terms of Use will be deemed to grant you, by implication, estoppel or otherwise, a license to Our Technology. Certain of the names, logos, and other materials displayed via the Web site constitute trademarks, tradenames, service marks or logos (“Marks”) of us or other entities. You are not authorized to use any such Marks. Ownership of all such Marks and the goodwill associated therewith remains with us or those other entities. Any use of third party software provided in connection with the Web Site will be governed by such third parties’ licenses and not by these Site Terms of Use. Information on this Web Site may contain technical inaccuracies or typographical errors. Lenovo provides no assurances that any reported problems may be resolved with the use of any information that Lenovo provides """ model_dict = { 'model': model, 'max_length': 512, 'min_length': 4 } tokenizer_dict = { 'tokenizer': tokenizer, 'max_length': 1024 } print(get_summary(model_dict, tokenizer_dict, test_tos)) ```
Mirjam/test-finetuned
Mirjam
2022-01-20T15:14:18Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:04Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: test-finetuned 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. --> # test-finetuned This model is a fine-tuned version of [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test) 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 1 | nan | 33.8462 | 31.746 | 30.7692 | 30.7692 | 86.0 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1 - Datasets 1.15.1 - Tokenizers 0.10.3
huggingtweets/aevaeavaevevave
huggingtweets
2022-01-20T15:13:33Z
6
0
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: http://www.huggingtweets.com/aevaeavaevevave/1642691608974/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/1471448753353670660/T0h3zXn-_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">aeva</div> <div style="text-align: center; font-size: 14px;">@aevaeavaevevave</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 aeva. | Data | aeva | | --- | --- | | Tweets downloaded | 3184 | | Retweets | 985 | | Short tweets | 659 | | Tweets kept | 1540 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g4kejp0/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 @aevaeavaevevave's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ikuw0pg) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ikuw0pg/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/aevaeavaevevave') 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)
pitehu/T5_NER_CONLL_LIST
pitehu
2022-01-20T14:32:20Z
12
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "Named Entity Recognition", "en", "dataset:wmt19", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- language: - en tags: - Named Entity Recognition license: apache-2.0 datasets: - wmt19 metrics: - bleu - sacrebleu inference: parameters: max_length: 1024 ---
aviator-neural/mbart_jokes
aviator-neural
2022-01-20T14:31:08Z
7
0
transformers
[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: mbart_jokes 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. --> # mbart_jokes This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0282 ## Model description This model is trained of jokes dataset , where you can ask a question and the model gives funny answer. ## Intended uses & limitations ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.3455 | 1.0 | 1914 | 3.0282 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
g30rv17ys/avhubert
g30rv17ys
2022-01-20T13:07:45Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
https://dl.fbaipublicfiles.com/avhubert/model/lrs3_vox/vsr/base_vox_433h.pt
mptrigo/run1
mptrigo
2022-01-20T10:37:49Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model_index: - name: run1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation metric: name: Bleu type: bleu value: 8.4217 --- <!-- 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. --> # run1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co/Helsinki-NLP/opus-mt-es-es) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 3.1740 - Bleu: 8.4217 - Gen Len: 15.9457 ## 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: 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: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 250 | 4.2342 | 0.8889 | 83.4022 | | 4.6818 | 2.0 | 500 | 3.7009 | 4.1671 | 35.587 | | 4.6818 | 3.0 | 750 | 3.4737 | 7.6414 | 23.9674 | | 3.4911 | 4.0 | 1000 | 3.3713 | 7.7512 | 18.6957 | | 3.4911 | 5.0 | 1250 | 3.2689 | 8.0901 | 19.4674 | | 3.0164 | 6.0 | 1500 | 3.2194 | 8.5708 | 25.0543 | | 3.0164 | 7.0 | 1750 | 3.1853 | 9.5275 | 23.9239 | | 2.6954 | 8.0 | 2000 | 3.1562 | 8.5635 | 18.9674 | | 2.6954 | 9.0 | 2250 | 3.1564 | 8.2031 | 17.5978 | | 2.4503 | 10.0 | 2500 | 3.1314 | 8.5638 | 18.1522 | | 2.4503 | 11.0 | 2750 | 3.1511 | 8.8428 | 17.913 | | 2.2554 | 12.0 | 3000 | 3.1513 | 8.1244 | 17.0 | | 2.2554 | 13.0 | 3250 | 3.1664 | 8.0157 | 16.2717 | | 2.1202 | 14.0 | 3500 | 3.1656 | 8.7758 | 16.6087 | | 2.1202 | 15.0 | 3750 | 3.1550 | 8.4637 | 16.4565 | | 2.0082 | 16.0 | 4000 | 3.1702 | 8.2488 | 15.8587 | | 2.0082 | 17.0 | 4250 | 3.1725 | 8.609 | 16.3043 | | 1.9274 | 18.0 | 4500 | 3.1750 | 8.4476 | 15.8043 | | 1.9274 | 19.0 | 4750 | 3.1734 | 8.4753 | 16.5543 | | 1.888 | 20.0 | 5000 | 3.1740 | 8.4217 | 15.9457 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.1.dev0 - Tokenizers 0.10.3
dbsamu/distilbert-base-uncased-finetuned-ner
dbsamu
2022-01-20T10:30:26Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:wikiann", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikiann metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: wikiann type: wikiann args: en metrics: - name: Precision type: precision value: 0.8120642485217545 - name: Recall type: recall value: 0.830235495804385 - name: F1 type: f1 value: 0.8210493441599 - name: Accuracy type: accuracy value: 0.9203828724683252 --- <!-- 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-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.2781 - Precision: 0.8121 - Recall: 0.8302 - F1: 0.8210 - Accuracy: 0.9204 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3504 | 1.0 | 1250 | 0.2922 | 0.7930 | 0.8075 | 0.8002 | 0.9115 | | 0.2353 | 2.0 | 2500 | 0.2711 | 0.8127 | 0.8264 | 0.8195 | 0.9196 | | 0.1745 | 3.0 | 3750 | 0.2781 | 0.8121 | 0.8302 | 0.8210 | 0.9204 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
dehio/german-qg-t5-e2e-quad
dehio
2022-01-20T09:40:47Z
5
3
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "question generation", "de", "dataset:deepset/germanquad", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- license: mit widget: - text: "Naturschutzwarte haben auf der ostfriesischen Insel Wangerooge zwei seltene Kurzschnäuzige Seepferdchen entdeckt. Die Tiere seien vergangene Woche bei einer sogenannten Spülsaumkontrolle entdeckt worden, bei der die Strände eigentlich nach Müll und toten Vögeln abgesucht würden, sagte der Geschäftsführer der zuständigen Naturschutz- und Forschungsgemeinschaft Mellumrat, Mathias Heckroth. Dabei seien den Naturschützern am Nordstrand kurz hintereinander die beiden leblosen, nur wenige Zentimeter großen Tiere aufgefallen. Experten der Nationalparkverwaltung bestimmten beide Tiere als Kurzschnäuzige Seepferdchen (Hippocampus hippocampus)." inference: parameters: max_length: 128 language: - de tags: - question generation datasets: - deepset/germanquad model-index: - name: german-qg-t5-e2e-quad 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. --> # german-qg-t5-e2e-quad (Work in progress) This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of [valhalla/t5-base-e2e-qg](https://huggingface.co/valhalla/t5-base-e2e-qg) on the [GermanQuAD dataset from deepset](https://huggingface.co/datasets/deepset/germanquad). ## Model description More information needed ## Training and evaluation data Bleu_1: 0.196051 Bleu_2: 0.122380 Bleu_3: 0.079980 Bleu_4: 0.053672 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
hrdipto/wav2vec2-xls-r-tf-left-right-shuru
hrdipto
2022-01-20T08:48:17Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-xls-r-tf-left-right-shuru 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-xls-r-tf-left-right-shuru This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0921 - Wer: 1.2628 ## 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: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.5528 | 23.81 | 500 | 0.5509 | 1.9487 | | 0.2926 | 47.62 | 1000 | 0.1306 | 1.2756 | | 0.1171 | 71.43 | 1500 | 0.1189 | 1.2628 | | 0.0681 | 95.24 | 2000 | 0.0921 | 1.2628 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
ml6team/robbert-dutch-base-toxic-comments
ml6team
2022-01-20T07:57:36Z
2,793
6
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "nl", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- language: - nl tags: - text-classification - pytorch widget: - text: "Ik heb je lief met heel mijn hart" example_title: "Non toxic comment 1" - text: "Dat is een goed punt, zo had ik het nog niet bekeken." example_title: "Non toxic comment 2" - text: "Wat de fuck zei je net tegen me, klootzak?" example_title: "Toxic comment 1" - text: "Rot op, vuile hoerenzoon." example_title: "Toxic comment 2" license: apache-2.0 metrics: - Accuracy, F1 Score, Recall, Precision --- # RobBERT-dutch-base-toxic-comments ## Model description: This model was created with the purpose to detect toxic or potentially harmful comments. For this model, we finetuned a dutch RobBerta-based model called [RobBERT](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the translated [Jigsaw Toxicity dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge). The original dataset was translated using the appropriate [MariantMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl). The model was trained for 2 epochs, on 90% of the dataset, with the following arguments: ``` training_args = TrainingArguments( learning_rate=1e-5, per_device_train_batch_size=8, per_device_eval_batch_size=8, gradient_accumulation_steps=6, load_best_model_at_end=True, metric_for_best_model="recall", epochs=2, evaluation_strategy="steps", save_strategy="steps", save_total_limit=10, logging_steps=100, eval_steps=250, save_steps=250, weight_decay=0.001, report_to="wandb") ``` ## Model Performance: Model evaluation was done on 1/10th of the dataset, which served as the test dataset. | Accuracy | F1 Score | Recall | Precision | | --- | --- | --- | --- | | 95.63 | 78.80 | 78.99 | 78.61 | ## Dataset: Unfortunately we cannot open-source the dataset, since we are bound by the underlying Jigsaw license.
abdelkader/distilbert-base-uncased-distilled-clinc
abdelkader
2022-01-20T05:15:31Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9464516129032258 --- <!-- 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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3038 - Accuracy: 0.9465 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 2.8460 | 0.7506 | | 3.322 | 2.0 | 636 | 1.4301 | 0.8532 | | 3.322 | 3.0 | 954 | 0.7377 | 0.9152 | | 1.2296 | 4.0 | 1272 | 0.4784 | 0.9316 | | 0.449 | 5.0 | 1590 | 0.3730 | 0.9390 | | 0.449 | 6.0 | 1908 | 0.3367 | 0.9429 | | 0.2424 | 7.0 | 2226 | 0.3163 | 0.9468 | | 0.1741 | 8.0 | 2544 | 0.3074 | 0.9452 | | 0.1741 | 9.0 | 2862 | 0.3054 | 0.9458 | | 0.1501 | 10.0 | 3180 | 0.3038 | 0.9465 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
UBC-NLP/ARBERT
UBC-NLP
2022-01-19T20:10:55Z
540
5
transformers
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "Arabic BERT", "MSA", "Twitter", "Masked Langauge Model", "ar", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- language: - ar tags: - Arabic BERT - MSA - Twitter - Masked Langauge Model widget: - text: "اللغة العربية هي لغة [MASK]." --- <img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="30%" height="30%" align="right"/> **ARBERT** is one of three models described in our **ACl 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://mageed.arts.ubc.ca/files/2020/12/marbert_arxiv_2020.pdf)**. ARBERT is a large-scale pre-trained masked language model focused on Modern Standard Arabic (MSA). To train ARBERT, we use the same architecture as BERT-base: 12 attention layers, each has 12 attention heads and 768 hidden dimensions, a vocabulary of 100K WordPieces, making ∼163M parameters. We train ARBERT on a collection of Arabic datasets comprising **61GB of text** (**6.2B tokens**). For more information, please visit our own GitHub [repo](https://github.com/UBC-NLP/marbert). # BibTex If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated): ```bibtex @inproceedings{abdul-mageed-etal-2021-arbert, title = "{ARBERT} {\&} {MARBERT}: Deep Bidirectional Transformers for {A}rabic", author = "Abdul-Mageed, Muhammad and Elmadany, AbdelRahim and Nagoudi, El Moatez Billah", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.551", doi = "10.18653/v1/2021.acl-long.551", pages = "7088--7105", abstract = "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.", } ``` ## Acknowledgments We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
hrdipto/wav2vec2-xls-r-tf-left-right-trainer
hrdipto
2022-01-19T20:06:38Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-xls-r-tf-left-right-trainer 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-xls-r-tf-left-right-trainer This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0090 - eval_wer: 0.0037 - eval_runtime: 11.2686 - eval_samples_per_second: 71.703 - eval_steps_per_second: 8.963 - epoch: 21.05 - step: 4000 ## 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: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
kjackson/distilbert-base-uncased-finetuned-emotion
kjackson
2022-01-19T19:10:27Z
0
0
null
[ "exbert", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1907.11692", "license:mit", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: en tags: - exbert license: mit datasets: - bookcorpus - wikipedia --- # RoBERTa base model Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it makes a difference between english and English. Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team.
vuiseng9/bert-base-squadv1
vuiseng9
2022-01-19T19:03:57Z
5
0
transformers
[ "transformers", "pytorch", "onnx", "bert", "question-answering", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
This model is a fork of [```csarron/bert-base-uncased-squad-v1```](https://huggingface.co/csarron/bert-base-uncased-squad-v1). ``` eval_exact_match = 80.9082 eval_f1 = 88.2275 eval_samples = 10784 ``` # Eval ```bash export CUDA_VISIBLE_DEVICES=0 OUTDIR=eval-bert-base-squadv1 WORKDIR=transformers/examples/pytorch/question-answering cd $WORKDIR nohup python run_qa.py \ --model_name_or_path vuiseng9/bert-base-squadv1 \ --dataset_name squad \ --do_eval \ --per_device_eval_batch_size 128 \ --max_seq_length 384 \ --doc_stride 128 \ --overwrite_output_dir \ --output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log & ```
masapasa/wav2vec2-large-xls-r-300m-turkish-colab
masapasa
2022-01-19T17:30:55Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-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-large-xls-r-300m-turkish-colab 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: 50 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.13.3 - Tokenizers 0.10.3
dehio/german-qg-t5-drink600
dehio
2022-01-19T16:38:22Z
7
1
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "question generation", "de", "dataset:deepset/germanquad", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- license: mit widget: - text: "generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert." language: - de tags: - question generation datasets: - deepset/germanquad model-index: - name: german-qg-t5-drink600 results: [] --- # german-qg-t5-drink600 This model is fine-tuned in question generation in German. The expected answer must be highlighted with &lt;hl> token. It is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad) and further pre-trained on drink related questions. ## Task example #### Input generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl &lt;hl>im Sommer wie auch zu Silvester&lt;hl> funktioniert. #### Expected Question Zu welchen Gelegenheiten passt der Monk Sour gut? ## Model description The model is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad), which was pre-trained on [GermanQUAD](https://www.deepset.ai/germanquad). We further pre-trained it on questions annotated on drink receipts from [Mixology](https://mixology.eu/) ("drink600"). We have not yet open sourced the dataset, since we do not own copyright on the source material. ## Training and evaluation data The training script can be accessed [here](https://github.com/d-e-h-i-o/german-qg). ## Evaluation It achieves a **BLEU-4 score of 29.80** on the drink600 test set (n=120) and **11.30** on the GermanQUAD test set. Thus, fine-tuning on drink600 did not affect performance on GermanQuAD. In comparison, *german-qg-t5-quad* achieves a BLEU-4 score of **10.76** on the drink600 test set. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
indonesian-nlp/wav2vec2-luganda
indonesian-nlp
2022-01-19T16:19:45Z
11
2
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "lg", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: lg datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech license: apache-2.0 model-index: - name: Wav2Vec2 Luganda by Indonesian-NLP results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice lg type: common_voice args: lg metrics: - name: Test WER type: wer value: 7.53 --- # Automatic Speech Recognition for Luganda This is the model built for the [Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition). It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model on the [Luganda Common Voice dataset](https://huggingface.co/datasets/common_voice) version 7.0. We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/luganda-asr) to test the model. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "lg", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda") model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): if "audio" in batch: speech_array = torch.tensor(batch["audio"]["array"]) else: speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the Indonesian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "lg", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda") model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda") model.to("cuda") chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"] chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() if "audio" in batch: speech_array = torch.tensor(batch["audio"]["array"]) else: speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` WER without KenLM: 15.38 % WER With KenLM: **Test Result**: 7.53 % ## Training The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO The script used for training can be found [here](https://github.com/indonesian-nlp/luganda-asr)
DanL/scientific-challenges-and-directions
DanL
2022-01-19T12:47:22Z
315
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "en", "dataset:DanL/scientific-challenges-and-directions-dataset", "arxiv:2108.13751", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
--- tags: - generated_from_trainer - text-classification language: - en datasets: - DanL/scientific-challenges-and-directions-dataset widget: - text: "severe atypical cases of pneumonia emerged and quickly spread worldwide." example_title: "challenge" - text: "we speculate that studying IL-6 will be beneficial." example_title: "direction" - text: "in future studies, both PRRs should be tested as the cause for multiple deaths." example_title: "both" - text: "IbMADS1-transformed potatoes exhibited tuber morphogenesis in the fibrous roots." example_title: "neither" metrics: - precision - recall - f1 model-index: - name: scientific-challenges-and-directions results: [] --- # scientific-challenges-and-directions We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ and _directions_ are defined as follows: * **Challenge**: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. * **Research direction**: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. * This model here is described in our paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751) (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results). * Our dataset can be found [here](https://huggingface.co/datasets/DanL/scientific-challenges-and-directions-dataset). * Please cite our paper if you use our datasets or models in your project. See the [BibTeX](#citation). * Feel free to [email us](#contact-us). * Also, check out [our search engine](https://challenges.apps.allenai.org/), as an example application. ## Model description This model is a fine-tuned version of [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [scientific-challenges-and-directions-dataset](https://huggingface.co/datasets/DanL/scientific-challenges-and-directions-dataset), designed for multi-label text classification. ## Training and evaluation data The scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751) ## Example notebook We include an example notebook that uses the model for inference in our [repo](https://github.com/Dan-La/scientific-challenges-and-directions). See `Inference_Notebook.ipynb`. A training notebook is also included. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning rate: 2e-05 - train batch size: 8 - eval batch size: 4 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr scheduler type: linear - lr scheduler warmup steps: 500 - num epochs: 30 ### Training results The achieves the following results on the test set: - Precision Challenge: 0.768719 - Recall Challenge: 0.780405 - F1 Challenge: 0.774518 - Precision Direction: 0.758112 - Recall Direction: 0.774096 - F1 Direction: 0.766021 - Precision (micro avg. on both labels): 0.764894 - Recall (micro avg. on both labels): 0.778139 - F1 (micro avg. on both labels): 0.771459 ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3 ## Citation If using our dataset and models, please cite: ``` @misc{lahav2021search, title={A Search Engine for Discovery of Scientific Challenges and Directions}, author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope}, year={2021}, eprint={2108.13751}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Contact us Please don't hesitate to reach out. **Email:** `[email protected]`,`[email protected]`.
mishig/test_vid
mishig
2022-01-19T09:56:39Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
# Video demo on ModelCard Please find [this file](https://huggingface.co/mishig/test_vid/blob/main/README.md) to see how to add a video to model card. <video src="https://huggingface.co/mishig/test_vid/resolve/main/output.mp4" controls autoplay loop/>
chitra/finetuned-adversarial-paraphrase-model
chitra
2022-01-19T09:13:16Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- tags: - generated_from_trainer model-index: - name: finetuned-adversarial-paraphrase-model 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. --> # finetuned-adversarial-paraphrase-model This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingface.co/coderpotter/adversarial-paraphrasing-detector) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.5680 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0848 | 1.0 | 2000 | 5.4633 | | 0.0495 | 2.0 | 4000 | 6.0352 | | 0.0121 | 3.0 | 6000 | 7.5680 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
mrp/distilbert-base-uncased-finetuned-imdb
mrp
2022-01-19T08:44:09Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-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. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4718 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.707 | 1.0 | 157 | 2.4883 | | 2.572 | 2.0 | 314 | 2.4240 | | 2.5377 | 3.0 | 471 | 2.4355 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
huggingtweets/histronicmonstr
huggingtweets
2022-01-19T04:57:37Z
5
0
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: http://www.huggingtweets.com/histronicmonstr/1642568219493/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/1431060400171270149/X2agCkD0_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">(心) !!!Ma-tin Korii!!! Uwa～😃!!!</div> <div style="text-align: center; font-size: 14px;">@histronicmonstr</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 (心) !!!Ma-tin Korii!!! Uwa～😃!!!. | Data | (心) !!!Ma-tin Korii!!! Uwa～😃!!! | | --- | --- | | Tweets downloaded | 3203 | | Retweets | 97 | | Short tweets | 488 | | Tweets kept | 2618 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1sdp3pm6/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 @histronicmonstr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ms6e48p) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ms6e48p/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/histronicmonstr') 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)
chitra/finetune-paraphrase-model
chitra
2022-01-19T04:40:57Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- tags: - generated_from_trainer model-index: - name: finetune-paraphrase-model 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. --> # finetune-paraphrase-model This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingface.co/coderpotter/adversarial-paraphrasing-detector) on an unknown 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: 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: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.1 | 200 | 3.0116 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
huggingtweets/godslovepariah
huggingtweets
2022-01-19T04:12:22Z
3
0
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: http://www.huggingtweets.com/godslovepariah/1642565537762/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/1432780406777020417/XTrp9MCR_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">LOVER//PARIAH</div> <div style="text-align: center; font-size: 14px;">@godslovepariah</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 LOVER//PARIAH. | Data | LOVER//PARIAH | | --- | --- | | Tweets downloaded | 525 | | Retweets | 9 | | Short tweets | 10 | | Tweets kept | 506 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6l5fj9xw/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 @godslovepariah's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3v0x5r1a) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3v0x5r1a/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/godslovepariah') 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)
NbAiLab/roberta_des_128
NbAiLab
2022-01-19T01:06:51Z
3
0
transformers
[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:04Z
Just for performing some experiments. Do not use. This needed to be restarted at 100k. I am getting memory errors at the end of the epoch. Not really sure why. Step 2 is therefore on train_2__4. Static learning rate for a while. The first 100k ended at 0.59. This is decent so early. No point in running more epochs here though. Changing the corpus and continue training.
domdomreloaded/bert-base-uncased-finetuned-swag
domdomreloaded
2022-01-18T22:33:47Z
11
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "dataset:swag", "license:apache-2.0", "endpoints_compatible", "region:us" ]
multiple-choice
2022-03-02T23:29:05Z
--- 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: 0.6045 - Accuracy: 0.7960 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7494 | 1.0 | 4597 | 0.5942 | 0.7716 | | 0.3499 | 2.0 | 9194 | 0.6045 | 0.7960 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
malloc/OpenNMT-py-German-English-2-layer-BiLSTM
malloc
2022-01-18T20:22:23Z
0
0
null
[ "translation", "pytorch", "de", "en", "license:mit", "region:us" ]
translation
2022-03-02T23:29:05Z
--- language: - de - en tags: - translation - pytorch license: mit datasets: - IWSLT ‘14 DE-EN metrics: - bleu --- # OpenNMT-py-English-German-Transformer [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework. OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for German to English translation. - Configuration: 2-layer BiLSTM with hidden size 500 trained for 20 epochs - Data: IWSLT ‘14 DE-EN - BLEU: 30.33
malloc/OpenNMT-py-English-German-Transformer
malloc
2022-01-18T20:18:11Z
0
2
null
[ "translation", "pytorch", "de", "en", "dataset:WMT", "license:mit", "region:us" ]
translation
2022-03-02T23:29:05Z
--- language: - de - en tags: - translation - pytorch license: mit datasets: - WMT metrics: - bleu --- # OpenNMT-py-English-German-Transformer [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework. OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for English to German translation. - Configuration: Base Transformer configuration with [standard training options](http://opennmt.net/OpenNMT-py/FAQ.html#how-do-i-use-the-transformer-model-do-you-support-multi-gpu) - Data: WMT with shared SentencePiece model - BLEU: - newstest2014 = 26.89 - newstest2017 = 28.09
vuiseng9/bert-base-squadv1-pruneofa-90pc-bt
vuiseng9
2022-01-18T19:13:21Z
3
0
transformers
[ "transformers", "pytorch", "onnx", "bert", "question-answering", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
This model is transfer-learning of [bert-base pruneofa 90% sparse](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) on Squadv1 dataset. ``` eval_exact_match = 80.2933 eval_f1 = 87.6788 eval_samples = 10784 ``` # Train use https://github.com/IntelLabs/Model-Compression-Research-Package.git see ```pruneofa-transfer-learning.sh``` # Eval ```bash export CUDA_VISIBLE_DEVICES=0 OUTDIR=eval-bert-base-squadv1-pruneofa-90pc-bt WORKDIR=transformers/examples/pytorch/question-answering cd $WORKDIR nohup python run_qa.py \ --model_name_or_path vuiseng9/bert-base-squadv1-pruneofa-90pc-bt \ --dataset_name squad \ --do_eval \ --per_device_eval_batch_size 128 \ --max_seq_length 384 \ --doc_stride 128 \ --overwrite_output_dir \ --output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log & ```
huggingtweets/collision
huggingtweets
2022-01-18T17:17:28Z
5
0
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: http://www.huggingtweets.com/collision/1642526243846/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/2464132281/jbbxl9p7ratdyuposrif_400x400.jpeg&#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">John Collison</div> <div style="text-align: center; font-size: 14px;">@collision</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 John Collison. | Data | John Collison | | --- | --- | | Tweets downloaded | 3222 | | Retweets | 999 | | Short tweets | 206 | | Tweets kept | 2017 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ifqwdbm/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 @collision's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gdto8z3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gdto8z3/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/collision') 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)
tal-yifat/injury-report-test
tal-yifat
2022-01-18T16:24:00Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: injury-report-test 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. --> # injury-report-test 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: 1.5697 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.8158 | 1.0 | 6633 | 1.7368 | | 1.6984 | 2.0 | 13266 | 1.6198 | | 1.6209 | 3.0 | 19899 | 1.5800 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
phueb/BabyBERTa-2
phueb
2022-01-18T14:44:44Z
60
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "BabyBERTa", "en", "dataset:CHILDES", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- language: en tags: - BabyBERTa datasets: - CHILDES widget: - text: "Look here. What is that <mask> ?" - text: "Do you like your <mask> ?" --- ## BabyBERTA ### Overview BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input. It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed. The three provided models are randomly selected from 10 that were trained and reported in the paper. ## Loading the tokenizer BabyBERTa was trained with `add_prefix_space=True`, so it will not work properly with the tokenizer defaults. For instance, to load the tokenizer for BabyBERTa-1, load it as follows: ```python tokenizer = RobertaTokenizerFast.from_pretrained("phueb/BabyBERTa-1", add_prefix_space=True) ``` ### Hyper-Parameters See the paper for details. All provided models were trained for 400K steps with a batch size of 16. Importantly, BabyBERTa never predicts unmasked tokens during training - `unmask_prob` is set to zero. ### Performance BabyBerta was developed for learning grammatical knowledge from child-directed input. Its grammatical knowledge was evaluated using the [Zorro](https://github.com/phueb/Zorro) test suite. The best model achieves an overall accuracy of 80.3, comparable to RoBERTa-base, which achieves an overall accuracy of 82.6 on the latest version of Zorro (as of October, 2021). Both values differ slightly from those reported in the [CoNLL 2021 paper](https://aclanthology.org/2021.conll-1.49/). There are two reasons for this: 1. Performance of RoBERTa-base is slightly larger because the authors previously lower-cased all words in Zorro before evaluation. Lower-casing of proper nouns is detrimental to RoBERTa-base because RoBERTa-base has likely been trained on proper nouns that are primarily title-cased. In contrast, because BabyBERTa is not case-sensitive, its performance is not influenced by this change. 2. The latest version of Zorro no longer contains ambiguous content words such as "Spanish" which can be both a noun and an adjective. this resulted in a small reduction in the performance of BabyBERTa. Overall Accuracy on Zorro: | Model Name | Accuracy (holistic scoring) | Accuracy (MLM-scoring) | |----------------------------------------|------------------------------|------------| | [BabyBERTa-1][link-BabyBERTa-1] | 80.3 | 79.9 | | [BabyBERTa-2][link-BabyBERTa-2] | 78.6 | 78.2 | | [BabyBERTa-3][link-BabyBERTa-3] | 74.5 | 78.1 | ### Additional Information This model was trained by [Philip Huebner](https://philhuebner.com), currently at the [UIUC Language and Learning Lab](http://www.learninglanguagelab.org). More info can be found [here](https://github.com/phueb/BabyBERTa). [link-BabyBERTa-1]: https://huggingface.co/phueb/BabyBERTa-1 [link-BabyBERTa-2]: https://huggingface.co/phueb/BabyBERTa-2 [link-BabyBERTa-3]: https://huggingface.co/phueb/BabyBERTa-3
phueb/BabyBERTa-1
phueb
2022-01-18T14:44:02Z
56
2
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "BabyBERTa", "en", "dataset:CHILDES", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- language: en tags: - BabyBERTa datasets: - CHILDES widget: - text: "Look here. What is that <mask> ?" - text: "Do you like your <mask> ?" --- ## BabyBERTA ### Overview BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input. It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed. The three provided models are randomly selected from 10 that were trained and reported in the paper. ## Loading the tokenizer BabyBERTa was trained with `add_prefix_space=True`, so it will not work properly with the tokenizer defaults. For instance, to load the tokenizer for BabyBERTa-1, load it as follows: ```python tokenizer = RobertaTokenizerFast.from_pretrained("phueb/BabyBERTa-1", add_prefix_space=True) ``` ### Hyper-Parameters See the paper for details. All provided models were trained for 400K steps with a batch size of 16. Importantly, BabyBERTa never predicts unmasked tokens during training - `unmask_prob` is set to zero. ### Performance BabyBerta was developed for learning grammatical knowledge from child-directed input. Its grammatical knowledge was evaluated using the [Zorro](https://github.com/phueb/Zorro) test suite. The best model achieves an overall accuracy of 80.3, comparable to RoBERTa-base, which achieves an overall accuracy of 82.6 on the latest version of Zorro (as of October, 2021). Both values differ slightly from those reported in the [CoNLL 2021 paper](https://aclanthology.org/2021.conll-1.49/). There are two reasons for this: 1. Performance of RoBERTa-base is slightly larger because the authors previously lower-cased all words in Zorro before evaluation. Lower-casing of proper nouns is detrimental to RoBERTa-base because RoBERTa-base has likely been trained on proper nouns that are primarily title-cased. In contrast, because BabyBERTa is not case-sensitive, its performance is not influenced by this change. 2. The latest version of Zorro no longer contains ambiguous content words such as "Spanish" which can be both a noun and an adjective. this resulted in a small reduction in the performance of BabyBERTa. Overall Accuracy on Zorro: | Model Name | Accuracy (holistic scoring) | Accuracy (MLM-scoring) | |----------------------------------------|------------------------------|------------| | [BabyBERTa-1][link-BabyBERTa-1] | 80.3 | 79.9 | | [BabyBERTa-2][link-BabyBERTa-2] | 78.6 | 78.2 | | [BabyBERTa-3][link-BabyBERTa-3] | 74.5 | 78.1 | ### Additional Information This model was trained by [Philip Huebner](https://philhuebner.com), currently at the [UIUC Language and Learning Lab](http://www.learninglanguagelab.org). More info can be found [here](https://github.com/phueb/BabyBERTa). [link-BabyBERTa-1]: https://huggingface.co/phueb/BabyBERTa-1 [link-BabyBERTa-2]: https://huggingface.co/phueb/BabyBERTa-2 [link-BabyBERTa-3]: https://huggingface.co/phueb/BabyBERTa-3
soskok1288/Sas
soskok1288
2022-01-18T11:54:46Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
export enum PipelineType { "text-generation"}
hkunlp/T5_large_prefix_all_tasks_2upsample2
hkunlp
2022-01-18T07:15:22Z
4
2
transformers
[ "transformers", "pytorch", "t5", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
This is the ckpt of prefix-tuning model we trained on 21 tasks using a upsampling temp of 2. Note: The prefix module is large due to the fact we keep the re-param weight and didn't compress it to make it more original and extendable for researchers.
csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10
csukuangfj
2022-01-18T04:29:27Z
0
0
null
[ "region:us" ]
null
2022-03-02T23:29:05Z
# Introduction ## How to clone this repo ``` sudo apt-get install git-lfs git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10 cd icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10 git lfs pull ``` **Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later. The model in this repo is trained using the commit `4c1b3665ee6efb935f4dd93a80ff0e154b13efb6`. You can use ``` git clone https://github.com/k2-fsa/icefall cd icefall git checkout 4c1b3665ee6efb935f4dd93a80ff0e154b13efb6 ``` to download `icefall`. You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/273e5fb2f3ac2620bafdffe2689b8b3ee10173d3/egs/librispeech/ASR/transducer_stateless/train.py#L198>. In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward; the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2. The decoder architecture is modified from [Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419). A Conv1d layer is placed right after the input embedding layer. ----- ## Description This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless and contains only an embedding layer and a Conv1d. The commands for training are: ``` cd egs/librispeech/ASR/ ./prepare.sh export CUDA_VISIBLE_DEVICES="0,1,2,3" ./transducer_stateless/train.py \ --world-size 4 \ --num-epochs 76 \ --start-epoch 0 \ --exp-dir transducer_stateless/exp-full \ --full-libri 1 \ --max-duration 250 \ --lr-factor 3 ``` The tensorboard training log can be found at <https://tensorboard.dev/experiment/qGdqzHnxS0WJ695OXfZDzA/> The command for decoding is: ``` epoch=71 avg=15 ## greedy search ./transducer_stateless/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir transducer_stateless/exp-full \ --bpe-model ./data/lang_bpe_500/bpe.model \ --max-duration 100 ## beam search ./transducer_stateless/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir transducer_stateless/exp-full \ --bpe-model ./data/lang_bpe_500/bpe.model \ --max-duration 100 \ --decoding-method beam_search \ --beam-size 4 ``` You can find the decoding log for the above command in this repo (in the folder `log`). The WERs for the test datasets are | | test-clean | test-other | comment | |---------------------------|------------|------------|------------------------------------------| | greedy search | 2.69 | 6.81 | --epoch 71, --avg 15, --max-duration 100 | | beam search (beam size 4) | 2.68 | 6.72 | --epoch 71, --avg 15, --max-duration 100 | # File description - [log][log], this directory contains the decoding log and decoding results - [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model - [data][data], this directory contains files generated by [prepare.sh][prepare] - [exp][exp], this directory contains only one file: `preprained.pt` `exp/pretrained.pt` is generated by the following command: ``` ./transducer_stateless/export.py \ --epoch 71 \ --avg 15 \ --bpe-model data/lang_bpe_500/bpe.model \ --exp-dir transducer_stateless/exp-full ``` **HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other, just do the following: ``` cp icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/exp/pretrained.pt \ /path/to/icefall/egs/librispeech/ASR/transducer_stateless/exp/epoch-999.pt ``` and pass `--epoch 999 --avg 1` to `transducer_stateless/decode.py`. [icefall]: https://github.com/k2-fsa/icefall [prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh [exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/tree/main/exp [data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/tree/main/data [test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/tree/main/test_wavs [log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/tree/main/log [icefall]: https://github.com/k2-fsa/icefall
huggingtweets/dankogai-hirox246-syakkin_dama
huggingtweets
2022-01-18T02:01:17Z
0
0
null
[ "huggingtweets", "en", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: en thumbnail: http://www.huggingtweets.com/dankogai-hirox246-syakkin_dama/1642471272927/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/646595746905620480/oeKI14gB_400x400.png&#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/1190142566831984640/o4kO2hp-_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/1283621672541536259/WI_8OTJz_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">ひろゆき, Hiroyuki Nishimura & Dan Kogai & 借金玉</div> <div style="text-align: center; font-size: 14px;">@dankogai-hirox246-syakkin_dama</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 ひろゆき, Hiroyuki Nishimura & Dan Kogai & 借金玉. | Data | ひろゆき, Hiroyuki Nishimura | Dan Kogai | 借金玉 | | --- | --- | --- | --- | | Tweets downloaded | 3249 | 3250 | 3249 | | Retweets | 283 | 341 | 260 | | Short tweets | 1819 | 2313 | 2918 | | Tweets kept | 1147 | 596 | 71 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1meoqt2b/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 @dankogai-hirox246-syakkin_dama's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gc1ic0l) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gc1ic0l/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/dankogai-hirox246-syakkin_dama') 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)
jkang/drawing-artistic-trend-classifier
jkang
2022-01-18T01:19:29Z
3
0
tf-keras
[ "tf-keras", "en", "license:mit", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: en license: mit datasets: - web crawled (coming soon) --- # Simple CNN-based Artist Classifier This repo contains a simple CNN-based Keras model which classifies images into one of 8 artistic trends. See also: `https://huggingface.co/jkang/drawing-artist-classifier` - The purpose of this model was for a quick prototyping - Data has been web-crawled using `https://github.com/YoongiKim/AutoCrawler` - 8 popular artists/painters were chosen: - \[TREND\]: \[ID\] - cubism: 0, - expressionism: 1, - fauvisme: 2, - graffitiar: 3, - impressionism: 4, - popart: 5, - post_impressionism: 6, - surrealism: 7} - About 100 representative paintings per artist considering 8 trends were crawled and manually checked - Dataset will be shared later # How to use ```python import tensorflow as tf from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("jkang/drawing-artistic-trend-classifier") image_file = 'monet.jpg' img = tf.io.read_file(image_file) img = tf.io.decode_jpeg(img, channels=3) last_layer_activation, predictions = model(img[tf.newaxis,...]) ``` # Intended uses & limitations You can use this model freely for predicting artists or trends of a given image. Please keep in mind that this model is not intended for production, but for research and quick prototyping. Web-crawled image data might not have a balanced amount of drawings that sufficiently represent the artists. --- - 2022-01-18 first created by jaekoo kang
Huertas97/en_roberta_base_leetspeak_ner
Huertas97
2022-01-17T21:54:01Z
5
1
spacy
[ "spacy", "token-classification", "en", "license:apache-2.0", "model-index", "region:us" ]
token-classification
2022-03-02T23:29:04Z
--- tags: - spacy - token-classification language: - en license: apache-2.0 widget: - text: "But one other thing that we have to re;think is the way that we dy£ our #c!l.o|th?£+s." example_title: "Word camouflage detection" model-index: - name: en_roberta_base_leetspeak_ner results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.7966001851 - name: NER Recall type: recall value: 0.8619559279 - name: NER F Score type: f_score value: 0.8279903783 --- | Feature | Description | | --- | --- | | **Name** | `en_roberta_base_leetspeak_ner` | | **Version** | `0.0.0` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `transformer`, `ner` | | **Components** | `transformer`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [roberta-base](https://huggingface.co/roberta-base) pre-trained model on English language using a masked language modeling (MLM) objective by Yinhan Liu et al. <br> [LeetSpeak-NER](https://huggingface.co/spaces/Huertas97/LeetSpeak-NER) app where this model is in production for countering information disorders| | **License** | Apache 2.0 | | **Author** | [Álvaro Huertas García](https://www.linkedin.com/in/alvaro-huertas-garcia/) at [AI+DA](http://aida.etsisi.upm.es/) | ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `INV_CAMO`, `LEETSPEAK`, `MIX`, `PUNCT_CAMO` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 82.80 | | `ENTS_P` | 79.66 | | `ENTS_R` | 86.20 | | `TRANSFORMER_LOSS` | 177808.42 | | `NER_LOSS` | 608427.31 |