# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tydymy/gena_lm_bert_base_human_classify", trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained("tydymy/gena_lm_bert_base_human_classify", trust_remote_code=True)Quick Links
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Model Card for gena_lm_bert_base_human_classify
Model Details
- Model Name: gena_lm_bert_base_human_classify
- Type: Transformer
- Main Application: [Brief description of the main application of the model, e.g., text classification, image recognition, etc.]
Training Data
- Description: [Brief description of the training data, including source, nature (text, images, etc.), and size.]
- Preprocessing: [Details of any preprocessing steps applied to the training data.]
Model Architecture
- Architecture Details: [Details about the model architecture, e.g., number of layers, type of layers, etc.]
- Framework Used: PyTorch
Training Procedure
- Epochs: 2
- Batch Size: 64
- Learning Rate: 2e-5
- Weight Decay: 0.01
Training Performance
Epoch 1:
- Training Loss: 0.088600
- Validation Loss: 0.079851
- Accuracy: 96.9529%
- F1 Score: 96.9840%
- Precision: 95.7133%
- Recall: 98.2889%
Epoch 2:
- Training Loss: 0.058800
- Validation Loss: 0.057442
- Accuracy: 98.0280%
- F1 Score: 98.0338%
- Precision: 97.4486%
- Recall: 98.6260%
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tydymy/gena_lm_bert_base_human_classify", trust_remote_code=True)