Text Classification
Transformers
Safetensors
English
roberta
sentiment-analysis
youtube-comments
podcast
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("randomone14/podcast-sentiment-3c")
model = AutoModelForSequenceClassification.from_pretrained("randomone14/podcast-sentiment-3c", device_map="auto")Quick Links
Podcast comment sentiment (3-class RoBERTa)
Fine-tuned RoBERTa classifier for 3-class sentiment of YouTube podcast comments.
Labels
| id | label |
|---|---|
| 0 | negative |
| 1 | neutral |
| 2 | positive |
Quick start
from transformers import pipeline
clf = pipeline("text-classification", model="randomone14/podcast-sentiment-3c")
clf("This episode was incredible — I learned so much.")
Source project
Trained as part of the PodcastLens comment-intelligence pipeline.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="randomone14/podcast-sentiment-3c")