Instructions to use Anandi99/Wright with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Anandi99/Wright with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Anandi99/Wright") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse filesThis model can parse through the given resume and can pull the required information. It can also categorize and summarize the candidate's experience and determine if the profile is a good fit for the job description
README.md
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: bigscience-bloom-rail-1.0
|
| 3 |
+
datasets:
|
| 4 |
+
- burberg92/resume_summary
|
| 5 |
+
- cnamuangtoun/resume-job-description-fit
|
| 6 |
+
- Sachinkelenjaguri/Resume_dataset
|
| 7 |
+
- bhuvanmdev/resume_parser
|
| 8 |
+
language:
|
| 9 |
+
- en
|
| 10 |
+
base_model:
|
| 11 |
+
- openai-community/gpt2
|
| 12 |
+
pipeline_tag: text-generation
|
| 13 |
+
library_name: sentence-transformers
|
| 14 |
+
---
|