Text Classification
Transformers
TensorBoard
Safetensors
distilbert
sentiment analysis
imdb
text-embeddings-inference
Instructions to use nppiech/an-imdb-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nppiech/an-imdb-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nppiech/an-imdb-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nppiech/an-imdb-classifier") model = AutoModelForSequenceClassification.from_pretrained("nppiech/an-imdb-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -18,7 +18,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 18 |
|
| 19 |
# an-imdb-classifier
|
| 20 |
|
| 21 |
-
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on
|
| 22 |
It achieves the following results on the evaluation set:
|
| 23 |
- Loss: 0.3635
|
| 24 |
- Accuracy: 0.898
|
|
|
|
| 18 |
|
| 19 |
# an-imdb-classifier
|
| 20 |
|
| 21 |
+
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the stanfordnlp.imdb dataset.
|
| 22 |
It achieves the following results on the evaluation set:
|
| 23 |
- Loss: 0.3635
|
| 24 |
- Accuracy: 0.898
|