Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Intel/MiniLM-L12-H384-uncased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/MiniLM-L12-H384-uncased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/MiniLM-L12-H384-uncased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/MiniLM-L12-H384-uncased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("Intel/MiniLM-L12-H384-uncased-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Intel/MiniLM-L12-H384-uncased-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc/resolve/main/README.md
- Command line
-
hf download hf://Intel/MiniLM-L12-H384-uncased-mrpc/README.md
-
curl -L -o README.md https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc/resolve/main/README.md
1.54 kB
metadata
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: MiniLM-L12-H384-uncased-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.875
- name: F1
type: f1
value: 0.9097345132743363
MiniLM-L12-H384-uncased-mrpc
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.4319
- Accuracy: 0.875
- F1: 0.9097
- Combined Score: 0.8924
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1