Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use autoevaluate/natural-language-inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/natural-language-inference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/natural-language-inference")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/natural-language-inference") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/natural-language-inference", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from autoevaluate/natural-language-inference: direct link, hf CLI and curl.
- Browser
- Download file 1.81 kB
-
https://huggingface.co/autoevaluate/natural-language-inference/resolve/main/README.md
- Command line
-
hf download hf://autoevaluate/natural-language-inference/README.md
-
curl -L -o README.md https://huggingface.co/autoevaluate/natural-language-inference/resolve/main/README.md
1.81 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: natural-language-inference
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: train
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8284313725490197
- name: F1
type: f1
value: 0.8821548821548822
natural-language-inference
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.4120
- Accuracy: 0.8284
- F1: 0.8822
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 230 | 0.4288 | 0.8039 | 0.8644 |
| No log | 2.0 | 460 | 0.4120 | 0.8284 | 0.8822 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1