Instructions to use tceron/info-seek-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tceron/info-seek-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tceron/info-seek-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tceron/info-seek-classifier") model = AutoModelForSequenceClassification.from_pretrained("tceron/info-seek-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,6 +1,15 @@
|
|
| 1 |
---
|
| 2 |
library_name: transformers
|
| 3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
---
|
| 5 |
|
| 6 |
# Model Card for Model ID
|
|
@@ -23,7 +32,22 @@ This classifier has been trained with ModernBERT large. It classifies user inter
|
|
| 23 |
|
| 24 |
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
|
| 25 |
|
| 26 |
-
- **Developed by:**
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 27 |
- **Funded by [optional]:** [More Information Needed]
|
| 28 |
- **Shared by [optional]:** [More Information Needed]
|
| 29 |
- **Model type:** [More Information Needed]
|
|
|
|
| 1 |
---
|
| 2 |
library_name: transformers
|
| 3 |
+
license: odc-by
|
| 4 |
+
datasets:
|
| 5 |
+
- tceron/wildseek
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
metrics:
|
| 9 |
+
- f1
|
| 10 |
+
base_model:
|
| 11 |
+
- answerdotai/ModernBERT-large
|
| 12 |
+
pipeline_tag: text-classification
|
| 13 |
---
|
| 14 |
|
| 15 |
# Model Card for Model ID
|
|
|
|
| 32 |
|
| 33 |
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
|
| 34 |
|
| 35 |
+
- **Developed by:**
|
| 36 |
+
|
| 37 |
+
All details in:
|
| 38 |
+
```bibtex
|
| 39 |
+
@inproceedings{ceron2026wildseek,
|
| 40 |
+
title = {WildSEEK: Evaluating Language Models for Information-Seeking},
|
| 41 |
+
author = {Ceron, Tanise and Baumann, Joachim and Bassignana, Elisa and Cabuk, Berat and Hovy, Dirk and Nozza, Debora},
|
| 42 |
+
booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
|
| 43 |
+
year = {2026}
|
| 44 |
+
}
|
| 45 |
+
```
|
| 46 |
+
|
| 47 |
+
Other classifiers from this study are:
|
| 48 |
+
- **`tceron/info-seek-classifier`** — information seeking, content creation, coding, not english, no request
|
| 49 |
+
- **`tceron/open-endedness-classifier`** — Analytical, Factual
|
| 50 |
+
|
| 51 |
- **Funded by [optional]:** [More Information Needed]
|
| 52 |
- **Shared by [optional]:** [More Information Needed]
|
| 53 |
- **Model type:** [More Information Needed]
|