Instructions to use ArnavL/twteval-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArnavL/twteval-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ArnavL/twteval-pretrained")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ArnavL/twteval-pretrained") model = AutoModelForMaskedLM.from_pretrained("ArnavL/twteval-pretrained") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Update dataset YAML metadata for model
#1
by librarian-bot - opened
README.md
CHANGED
|
@@ -1,11 +1,12 @@
|
|
| 1 |
-
---
|
| 2 |
-
license: mit
|
| 3 |
-
--
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
datasets: ArnavL/TWTEval-Pretraining-Processed
|
| 4 |
+
---
|
| 5 |
+
|
| 6 |
+
# Pretrained Model
|
| 7 |
+
|
| 8 |
+
BASE MODEL : BERT-BASE-UNCASED
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
DATASET : [TWTEVAL SENTIMENT](https://huggingface.co/datasets/ArnavL/TWTEval-Pretraining-Processed)
|
| 12 |
+
|