Instructions to use dejanseo/sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dejanseo/sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dejanseo/sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dejanseo/sentiment") model = AutoModelForSequenceClassification.from_pretrained("dejanseo/sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,45 +1,65 @@
|
|
| 1 |
---
|
| 2 |
license: bigscience-openrail-m
|
| 3 |
pipeline_tag: text-classification
|
| 4 |
-
|
| 5 |
widget:
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 43 |
---
|
| 44 |
Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).
|
| 45 |
|
|
|
|
| 1 |
---
|
| 2 |
license: bigscience-openrail-m
|
| 3 |
pipeline_tag: text-classification
|
|
|
|
| 4 |
widget:
|
| 5 |
+
- example_title: Very Positive
|
| 6 |
+
text: This product is absolutely amazing!
|
| 7 |
+
- example_title: Positive
|
| 8 |
+
text: I like the features of this app.
|
| 9 |
+
- example_title: Somewhat Positive
|
| 10 |
+
text: The service is pretty good.
|
| 11 |
+
- example_title: Neutral
|
| 12 |
+
text: It meets my expectations.
|
| 13 |
+
- example_title: Somewhat Negative
|
| 14 |
+
text: The experience was not as bad.
|
| 15 |
+
- example_title: Negative
|
| 16 |
+
text: I'm disappointed with the quality.
|
| 17 |
+
- example_title: Very Negative
|
| 18 |
+
text: This is the worst purchase I've ever made.
|
| 19 |
+
- example_title: Very Positive
|
| 20 |
+
text: >-
|
| 21 |
+
I recently bought a new sports jersey from this store, and it's absolutely
|
| 22 |
+
amazing! The fabric is high quality, the fit is perfect, and the design
|
| 23 |
+
looks even better in person. I'm thrilled with my purchase and highly
|
| 24 |
+
recommend this store to anyone looking for custom sports apparel.
|
| 25 |
+
- example_title: Positive
|
| 26 |
+
text: >-
|
| 27 |
+
I tried out the new fitness app, and I like its features. It has a
|
| 28 |
+
user-friendly interface, plenty of workout plans, and tracks my progress
|
| 29 |
+
effectively. It has definitely added value to my fitness routine, and I find
|
| 30 |
+
it motivating to use daily.
|
| 31 |
+
- example_title: Somewhat Positive
|
| 32 |
+
text: >-
|
| 33 |
+
The customer service at this cafe was pretty good. They were polite and
|
| 34 |
+
attended to my needs promptly. The coffee tasted nice, and the atmosphere
|
| 35 |
+
was cozy. I enjoyed my visit and would consider coming back, although there
|
| 36 |
+
is room for improvement in the menu variety.
|
| 37 |
+
- example_title: Neutral
|
| 38 |
+
text: >-
|
| 39 |
+
The smartphone I purchased meets my expectations. It has all the features I
|
| 40 |
+
need and performs adequately. The battery life is decent, and the camera
|
| 41 |
+
quality is average. Overall, it serves its purpose well without any standout
|
| 42 |
+
qualities or significant drawbacks.
|
| 43 |
+
- example_title: Somewhat Negative
|
| 44 |
+
text: >-
|
| 45 |
+
The hotel stay was not as bad as I had feared, but there were some issues.
|
| 46 |
+
The room was clean, but the service was slow, and the amenities were
|
| 47 |
+
limited. It wasn't a terrible experience, but I expected more for the price
|
| 48 |
+
I paid.
|
| 49 |
+
- example_title: Negative
|
| 50 |
+
text: >-
|
| 51 |
+
I'm disappointed with the quality of this product. It feels flimsy and
|
| 52 |
+
cheaply made. After only a few uses, it started showing signs of wear and
|
| 53 |
+
tear. I expected better durability and performance based on the reviews. I
|
| 54 |
+
wouldn't recommend this to others.
|
| 55 |
+
- example_title: Very Negative
|
| 56 |
+
text: >-
|
| 57 |
+
This is the worst purchase I've ever made. The item arrived broken, and the
|
| 58 |
+
customer service was unresponsive. It was a complete waste of money, and I
|
| 59 |
+
regret buying it. The overall experience was frustrating and disappointing.
|
| 60 |
+
Avoid this product at all costs.
|
| 61 |
+
language:
|
| 62 |
+
- en
|
| 63 |
---
|
| 64 |
Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).
|
| 65 |
|