Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gokulsrinivasagan/tinybert_base_train_kd_mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokulsrinivasagan/tinybert_base_train_kd_mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokulsrinivasagan/tinybert_base_train_kd_mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_kd_mnli") model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_kd_mnli") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 9.0, | |
| "epoch_mm": 9.0, | |
| "eval_accuracy": 0.7492613346917982, | |
| "eval_accuracy_mm": 0.7617982099267697, | |
| "eval_loss": 0.6076479554176331, | |
| "eval_loss_mm": 0.5858094692230225, | |
| "eval_runtime": 5.5763, | |
| "eval_runtime_mm": 5.4141, | |
| "eval_samples": 9815, | |
| "eval_samples_mm": 9832, | |
| "eval_samples_per_second": 1760.132, | |
| "eval_samples_per_second_mm": 1816.015, | |
| "eval_steps_per_second": 6.994, | |
| "eval_steps_per_second_mm": 7.203 | |
| } |