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🔍 **MEMFOF** is a **memory-efficient optical flow method** for **Full HD video** that combines **high accuracy** with **low VRAM usage**. ✅ **Note:** This particular checkpoint is intended **for real-world videos** — it is trained with **higher diversity and robustness** in mind. Then use the following snippet to compute backward and forward optical flow for three consecutive frames:
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The model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english for a Speech Emotion Recognition (SER) task. The dataset used to fine-tune the original pre-trained model is the RAVDESS dataset. This dataset provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are: The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP
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**SecureBERT** is a RoBERTa-based, domain-specific language model trained on a large cybersecurity-focused corpus. It is designed to represent and understand cybersecurity text more effectively than general-purpose models. SecureBERT can be used as a base model for downstream NLP tasks in cybersecurity, including: - Text classification - Named Entity Recognition (NER) - Sequence-to-sequence tasks - Question answering - Outperforms baseline models such as **RoBERTa (base/large)**, **SciBERT**, and **SecBERT** in masked language modeling tasks within the cybersecurity domain. - Maintains strong performance in **general English language understanding**, ensuring broad usability beyond domain-specific tasks.
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