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---
license: cc-by-nc-sa-4.0
language:
- 'no'
---
# Model Card
NorGPT-3B-continue-NO-MRPC-peft is trained on top of [NorGPT-3B-continue](https://huggingface.co/NorGLM/NorGPT-3B-continue) model on [NO-MRPC](https://huggingface.co/datasets/NorGLM/NO-MRPC) dataset.
Data format:
```
input: {text_a}[SEP]{text_b}
label: {0, 1}
```
## Run the Model
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
source_model_id = "NorGPT-3B-continue"
peft_model_id = "NorGLM/NorGPT-3B-continue-NO-MRPC-peft"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced')
tokenizer_max_len = 2048
tokenizer_config = {'pretrained_model_name_or_path': source_model_id,
'max_len': tokenizer_max_len}
tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config)
tokenizer.pad_token = tokenizer.eos_token
model = PeftModel.from_pretrained(model, peft_model_id)
```
## Inference Example
Load the model to evaluate on the validation set:
```python
def getDataSetFromFiles(df):
# convert dataset
df["text"] = df[["text_a", "text_b"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1)
df = df.drop(["idx", "text_a", "text_b"], axis=1)
df["label"] = df.label.map({0: 0, 1: 1})
return Dataset.from_pandas(df)
print("--LOADING EVAL DATAS---")
eval_data = load_dataset("NorGLM/NO-MRPC", data_files="val.jsonl")
eval_data = getDataSetFromFiles(eval_data["train"].to_pandas())
print("--MAKING PREDICTIONS---")
model.eval()
y_true = []
y_pred = []
count = 0
for data in eval_data:
count = count + 1
if count % 100 == 0:
print(count)
inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device)
with torch.no_grad():
logits = model(**inputs).logits
#print(logits)
predicted_class_id = logits.argmax().item()
y_true.append(data['label'])
y_pred.append(predicted_class_id)
print(y_pred)
print(f"Lenght of true_values: {len(y_true)}")
print(f"Lenght of predicted_values: {len(y_pred)}")
y_true = np.array(y_true)
y_pred = np.array(y_pred)
F_score = f1_score(y_true, y_pred, average="macro")
print(f"F1 score: {F_score}")
accuracy = accuracy_score(y_true, y_pred)
print(f"Accuracy: {accuracy}")
```
## Note
More training details will be released soon! |