Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use lschlessinger/bert-finetuned-math-prob-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lschlessinger/bert-finetuned-math-prob-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lschlessinger/bert-finetuned-math-prob-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lschlessinger/bert-finetuned-math-prob-classification") model = AutoModelForSequenceClassification.from_pretrained("lschlessinger/bert-finetuned-math-prob-classification") - Notebooks
- Google Colab
- Kaggle
Commit ·
2af3f11
1
Parent(s): 5ab7cfe
Update README.md
Browse files
README.md
CHANGED
|
@@ -9,9 +9,6 @@ model-index:
|
|
| 9 |
results: []
|
| 10 |
---
|
| 11 |
|
| 12 |
-
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 13 |
-
should probably proofread and complete it, then remove this comment. -->
|
| 14 |
-
|
| 15 |
# bert-finetuned-math-prob-classification
|
| 16 |
|
| 17 |
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the part of the [competition_math dataset](https://huggingface.co/datasets/competition_math). Specifically, it was trained as a multi-class multi-label model on the problem text. The problem types (labels) used here are "Counting & Probability", "Prealgebra", "Algebra", "Number Theory", "Geometry", "Intermediate Algebra", and "Precalculus".
|
|
|
|
| 9 |
results: []
|
| 10 |
---
|
| 11 |
|
|
|
|
|
|
|
|
|
|
| 12 |
# bert-finetuned-math-prob-classification
|
| 13 |
|
| 14 |
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the part of the [competition_math dataset](https://huggingface.co/datasets/competition_math). Specifically, it was trained as a multi-class multi-label model on the problem text. The problem types (labels) used here are "Counting & Probability", "Prealgebra", "Algebra", "Number Theory", "Geometry", "Intermediate Algebra", and "Precalculus".
|