
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("zeusfsx/title-instruction")
model = AutoModelForSequenceClassification.from_pretrained("zeusfsx/title-instruction")


classification = pipeline("text-classification", model=model, tokenizer=tokenizer, device="mps")  # for mac I used mps
```
{"language": ["uk"], "license": "cc-by-sa-4.0", "metrics": ["f1"], "pipeline_tag": "text-classification"}
transformers pytorch xlm-roberta text-classification uk license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #uk #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


## Usage

## Usage
TAGS
#transformers #pytorch #xlm-roberta #text-classification #uk #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Usage
TAGS
#transformers #pytorch #xlm-roberta #text-classification #uk #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
## Usage