Zero-Shot Classification
Transformers
Safetensors
English
switch_transformers
text-classification
Generated from Trainer
custom_code
Eval Results (legacy)
Instructions to use glamprou/switch-base-8-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use glamprou/switch-base-8-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="glamprou/switch-base-8-mnli", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("glamprou/switch-base-8-mnli", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("glamprou/switch-base-8-mnli", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
license: apache-2.0
base_model: google/switch-base-8
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: glue
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8757119609438568
pipeline_tag: zero-shot-classification
glue
This model is a fine-tuned version of google/switch-base-8 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.3472
- Accuracy: 0.8757
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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0