Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use tsessk/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsessk/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tsessk/content")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tsessk/content") model = AutoModelForSequenceClassification.from_pretrained("tsessk/content") - Notebooks
- Google Colab
- Kaggle
| This directory includes a few sample datasets to get you started. | |
| * `california_housing_data*.csv` is California housing data from the 1990 US | |
| Census; more information is available at: | |
| https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub | |
| * `mnist_*.csv` is a small sample of the | |
| [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is | |
| described at: http://yann.lecun.com/exdb/mnist/ | |
| * `anscombe.json` contains a copy of | |
| [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it | |
| was originally described in | |
| Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American | |
| Statistician. 27 (1): 17-21. JSTOR 2682899. | |
| and our copy was prepared by the | |
| [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json). | |