Saattrupdan
Rank:Average Model Cost: $0.0000
Number of Runs: 11,090
Models by this creator
nbailab-base-ner-scandi
nbailab-base-ner-scandi
The nbailab-base-ner-scandi model is a fine-tuned version of NbAiLab/nb-bert-base for Named Entity Recognition (NER) in Scandinavian languages. It has been trained on Danish, Norwegian, Swedish, Icelandic, and Faroese datasets. The model performs well on Scandinavian NER test datasets and is also capable of predicting English entities due to its training on English data. It is smaller and faster than previous state-of-the-art models. The model was trained using a learning rate of 2e-05, a batch size of 8, and an Adam optimizer. The training was performed for 1000 epochs using the Transformers, PyTorch, Datasets, and Tokenizers frameworks.
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10.5K
Huggingface
wav2vec2-xls-r-300m-ftspeech
wav2vec2-xls-r-300m-ftspeech
Platform did not provide a description for this model.
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415
Huggingface
verdict-classifier
$-/run
82
Huggingface
job-listing-relevance-model
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41
Huggingface
xlmr-base-texas-squad-da
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26
Huggingface
employment-contract-ner-da
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19
Huggingface
job-listing-filtering-model
job-listing-filtering-model
Platform did not provide a description for this model.
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14
Huggingface
verdict-classifier-en
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11
Huggingface
electra-small-qa-da
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10
Huggingface
xlmr-base-texas-squad-es
xlmr-base-texas-squad-es
TExAS-SQuAD-es This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-es dataset. It achieves the following results on the evaluation set: Exact match: xx.xx% F1-score: xx.xx% 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 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 num_epochs: 3 Training results Framework versions Transformers 4.12.2 Pytorch 1.8.1+cu101 Datasets 1.12.1 Tokenizers 0.10.3
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10
Huggingface