Instructions to use shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -101,4 +101,13 @@ You can view the github using the following link:
|
|
| 101 |
|
| 102 |
Simply click the link to open the github repo in your browser.
|
| 103 |
|
| 104 |
-
Check out the "Fine-tune LLM.pptx" file in the GitHub repo for the theory behind this code.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 101 |
|
| 102 |
Simply click the link to open the github repo in your browser.
|
| 103 |
|
| 104 |
+
Check out the "Fine-tune LLM.pptx" file in the GitHub repo for the theory behind this code.
|
| 105 |
+
|
| 106 |
+
|
| 107 |
+
@misc{huggingfaceShukdevdatta123twitterdistilbertbaseuncasedsentimentanalysisloratextclassificationHugging,
|
| 108 |
+
author = {Shukdev Datta},
|
| 109 |
+
title = {shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification 路 {H}ugging {F}ace --- huggingface.co},
|
| 110 |
+
howpublished = {\url{https://huggingface.co/shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification}},
|
| 111 |
+
year = {},
|
| 112 |
+
note = {[Accessed 02-09-2024]},
|
| 113 |
+
}
|