Text Classification
Transformers
Safetensors
English
roberta
facebook
sentiment
customer-support
huggingface
fine-tuned
Eval Results (legacy)
text-embeddings-inference
Instructions to use harshithan/fb-post-classifier-roberta_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshithan/fb-post-classifier-roberta_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harshithan/fb-post-classifier-roberta_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harshithan/fb-post-classifier-roberta_v1") model = AutoModelForSequenceClassification.from_pretrained("harshithan/fb-post-classifier-roberta_v1") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -67,8 +67,8 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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from torch.nn.functional import softmax
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import torch
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model = AutoModelForSequenceClassification.from_pretrained("harshithan/fb-post-classifier-
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tokenizer = AutoTokenizer.from_pretrained("harshithan/fb-post-classifier-
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inputs = tokenizer("I love the fast delivery!", return_tensors="pt")
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outputs = model(**inputs)
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from torch.nn.functional import softmax
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import torch
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model = AutoModelForSequenceClassification.from_pretrained("harshithan/fb-post-classifier-roberta_v1")
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tokenizer = AutoTokenizer.from_pretrained("harshithan/fb-post-classifier-roberta_v1")
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inputs = tokenizer("I love the fast delivery!", return_tensors="pt")
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outputs = model(**inputs)
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