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nci-ner-v2

synapti

This model is a fine-tuned version of synapti/nci-ner-v2-staging on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4872 - Precision: 0.8799 - Recall: 0.9043 - F1: 0.8920 The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.05 - num_epochs: 3 - label_smoothing_factor: 0.05

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This model is a fine-tuned version of syssec-utd/py311-pylingual-v1-mlm on the syssec-utd/segmentation-py311-pylingual-v1-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0046 - Precision: 0.9958 - Recall: 0.9937 - F1: 0.9948 - Accuracy: 0.9983 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP

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This model is a fine-tuned version of syssec-utd/py312-pylingual-v1-mlm on the syssec-utd/segmentation-py312-pylingual-v1-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0053 - Precision: 0.9923 - Recall: 0.9935 - F1: 0.9929 - Accuracy: 0.9982 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP

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This model is a fine-tuned version of syssec-utd/py313-pylingual-v3-mlm on the syssec-utd/segmentation-py313-pylingual-v3-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0047 - Precision: 0.9945 - Recall: 0.9944 - F1: 0.9945 - Accuracy: 0.9983 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 28 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP

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eu-pii-safeguard

tabularisai

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ModernFinBERT

tabularisai

A fine-tuned financial sentiment analysis model based on **ModernBERT**, trained on synthetic and real financial data cleaned through an automated AI agentic pipeline. The model covers diverse financial domains including news, tweets, crypto, and macroeconomics, making it the most general-purpose financial sentiment classifier. Benchmark results show superior performance with up to 48% accuracy improvement over existing models across multiple financial datasets. For private API access or access to even more powerful financial models, contact us at **info@tabularis.ai**

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](https://discord.gg/sznxwdqBXj) --> [](https://discord.gg/sznxwdqBXj) - 2025/8: Major model update +1 new language: **Swahili**! Also, general improvements accross all languages. - 2025/7: We’ve just released ModernFinBERT, a model we’ve been working on for a while. It’s built on the ModernBERT architecture and trained on a mix of real and synthetic data, with LLM-based label correction applied to public datasets to fix human annotation errors. It’s performing well across a range of benchmarks — in some cases improving accuracy by up to 48% over existing models like FinBERT. You can check it out here on Hugging Face: 👉 https://huggingface.co/tabularisai/ModernFinBERT

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