Text Classification
Transformers
PyTorch
Core ML
Safetensors
English
distilbert
text-embeddings-inference
Instructions to use Falconsai/intent_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/intent_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Falconsai/intent_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Falconsai/intent_classification") model = AutoModelForSequenceClassification.from_pretrained("Falconsai/intent_classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,6 +1,8 @@
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
pipeline_tag: text-classification
|
|
|
|
|
|
|
| 4 |
---
|
| 5 |
# Model Card: Fine-Tuned DistilBERT for User Intent Classification
|
| 6 |
|
|
@@ -64,5 +66,4 @@ It is essential to use this model responsibly and ethically, adhering to content
|
|
| 64 |
- [Hugging Face Model Hub](https://huggingface.co/models)
|
| 65 |
- [DistilBERT Paper](https://arxiv.org/abs/1910.01108)
|
| 66 |
|
| 67 |
-
**Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.
|
| 68 |
-
|
|
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
pipeline_tag: text-classification
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
---
|
| 7 |
# Model Card: Fine-Tuned DistilBERT for User Intent Classification
|
| 8 |
|
|
|
|
| 66 |
- [Hugging Face Model Hub](https://huggingface.co/models)
|
| 67 |
- [DistilBERT Paper](https://arxiv.org/abs/1910.01108)
|
| 68 |
|
| 69 |
+
**Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.
|
|
|