Update README.md

README.md

@@ -1,6 +1,40 @@
 ---
 library_name: peft
 ---
+## Description
+This model was obtained by fine-tuning the Llama-2 7B large language model with the LoRA technique. The aim is to develop a sentiment analysis system in Turkish language by training the model according to the sentences in the given data set.
+The evaluation metrics of the model were calculated and the following results were obtained.
+
+
+## Dataset
+
+The training data set consists of 152715 rows and the eval data set consists of 16968 rows. It includes social media posts and product reviews.
+
+
+## Uses
+from transformers import AutoConfig
+from transformers import AutoModelForSequenceClassification
+
+config = AutoConfig.from_pretrained("Minekorkmz/model_yurt_1200")
+num_labels = config.num_labels
+
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    "meta-llama/Llama-2-7b-chat-hf",
+    num_labels=num_labels
+)
+
+model = PeftModel.from_pretrained(base_model, "Minekorkmz/model_yurt_1200")
+tokenizer = AutoTokenizer.from_pretrained("Minekorkmz/model_yurt_1200")
+
+from transformers import pipeline
+
+sentiment_task = pipeline("sentiment-analysis",
+                         model=model,
+                         tokenizer=tokenizer,
+                         return_all_scores=True)
+
+print(sentiment_task("çok kötü bir ürün oldu sevemedim"))
+
 ## Training procedure
 
 
@@ -15,7 +49,12 @@ The following `bitsandbytes` quantization config was used during training:
 - bnb_4bit_quant_type: nf4
 - bnb_4bit_use_double_quant: True
 - bnb_4bit_compute_dtype: float16
-### Framework versions
 
 
+### Framework versions
+
 - PEFT 0.4.0
+- accelerate 0.26.0
+- bitsandbytes 0.41.1
+- transformers 4.35.0
+- trl 0.4.7