numind/NuNER v1.0 fine tuned on FewNERD fine supervised This is a NuNER model fine tuned on the NER ORGS dataset that can be used for Named Entity Recognition. NuNER model uses RoBERTa base as the backbone encoder and it was trained on the NuNER dataset, which is a large and diverse dataset synthetically labeled by gpt 3.5 turbo 0301 of 1M sentences. This further pre training phase allowed the generation of high quality token embeddings, a good starting point for fine tuning on more specialized datasets. Model Details The model was fine tuned as a regular BERT based model for NER task using HuggingFace Trainer class. Model labels Entity Types: ORG Uses Direct Use for Inference Training procedure Training hyperparameters The following hyperparameters were used during training: learning rate: 5e 05 train batch size: 32 eval batch size: 32 seed: 42 gradient accumulation steps: 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 ratio: 0.1 num epochs: 4 Training results Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy : : : : : : : : : : : : : : : : 0.0631 1.0 1710 0.0566 0.7635 0.7952…
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