Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ClaudiaRichard/all-MiniLM-L6-v2_mbti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClaudiaRichard/all-MiniLM-L6-v2_mbti with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ClaudiaRichard/all-MiniLM-L6-v2_mbti")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ClaudiaRichard/all-MiniLM-L6-v2_mbti") model = AutoModelForSequenceClassification.from_pretrained("ClaudiaRichard/all-MiniLM-L6-v2_mbti", device_map="auto") - Notebooks
- Google Colab
- Kaggle
all-MiniLM-L6-v2_mbti
This model is a fine-tuned version of sentence-transformers/all-MiniLM-L6-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5400
- F1: 0.6098
- Roc Auc: 0.6951
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc |
|---|---|---|---|---|---|
| 0.5423 | 1.0 | 5948 | 0.5398 | 0.5296 | 0.6572 |
| 0.5248 | 2.0 | 11896 | 0.5381 | 0.4742 | 0.6349 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for ClaudiaRichard/all-MiniLM-L6-v2_mbti
Base model
nreimers/MiniLM-L6-H384-uncased Quantized
sentence-transformers/all-MiniLM-L6-v2