add train code and requirements text file...

app.py

@@ -1,7 +1,123 @@
+import spaces
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# code
+import pandas as pd
+from datasets import load_dataset
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# from sentence_transformers import (
+#     SentenceTransformer,
+#     SentenceTransformerTrainer,
+#     SentenceTransformerTrainingArguments,
+#     SentenceTransformerModelCardData
+# ) ### we can imporet everhtuing from the main class...
+
+from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
+from sentence_transformers.losses import CachedMultipleNegativesRankingLoss
+from sentence_transformers.evaluation import InformationRetrievalEvaluator
+from sentence_transformers.training_args import SentenceTransformerTrainingArguments, BatchSamplers
+
+
+
+
+def get_ir_evaluator(eval_ds):
+    """create from anchor positive dataset instance... could make from a better dataset... LLM generate?"""
+
+    corpus = {}                                     
+    queries = {}
+    relevant_docs = {}                                    # relevant documents (qid => set[cid])
+    for idx, example in enumerate(eval_ds):
+        query = example['anchor']
+        queries[idx] = query
+
+        document = example['positive']
+        corpus[idx] = document
+
+        relevant_docs[idx] = set([idx])                   # note: should have more relevant docs here
+
+    ir_evaluator = InformationRetrievalEvaluator(
+        queries=queries,
+        corpus=corpus,
+        relevant_docs=relevant_docs,
+        name="ir-evaluator",
+    )
+    return ir_evaluator
+
+    
+
+
+@spaces.GPU(duration=3600)
+def train(hf_token, dataset_id, model_id, num_epochs, dev):
+
+    ds = load_dataset(dataset_id, split="train", token=hf_token)
+    ds = ds.shuffle(seed=42)
+    if len(ds) > 1000 and dev: ds = ds.select(range(0, 999))
+    ds = ds.train_test_split(train_size=0.75)
+    train_ds, eval_ds = ds['train'], ds['test']
+    print('train: ', len(train_ds), 'eval: ', len(eval_ds))
+
+    # model
+    model = SentenceTransformer(model_id)
+
+    # loss
+    loss = CachedMultipleNegativesRankingLoss(model)
+
+    # training args
+    args = SentenceTransformerTrainingArguments(
+        output_dir="outputs",                       # required 
+        num_train_epochs=num_epochs,                # optional...
+        per_device_train_batch_size=16,
+        warmup_ratio=0.1,
+        #fp16=True,                                  # Set to False if your GPU can't handle FP16
+        #bf16=False,                                 # Set to True if your GPU supports BF16
+        batch_sampler=BatchSamplers.NO_DUPLICATES,  # Losses using "in-batch negatives" benefit from no duplicates
+        save_total_limit=2
+        # per_device_eval_batch_size=1,
+        # eval_strategy="epoch",
+        # save_strategy="epoch",
+        # logging_steps=100,
+        # Optional tracking/debugging parameters:
+        # eval_strategy="steps",
+        # eval_steps=100,
+        # save_strategy="steps",
+        # save_steps=100,
+        # logging_steps=100,
+        # run_name="jina-code-vechain-pair",  # Used in W&B if `wandb` is installed
+    )
+
+    # ir evaluator
+    ir_evaluator = get_ir_evaluator(eval_ds)
+
+    # base model metrics
+    base_metrics = ir_evaluator(model)
+    print(ir_evaluator.primary_metric)
+    print(base_metrics[ir_evaluator.primary_metric])
+
+
+    # train
+    trainer = SentenceTransformerTrainer(
+        model=model,
+        args=args,
+        train_dataset=train_ds,
+        # eval_dataset=eval_ds,
+        loss=loss,
+        # evaluator=ir_evaluator,
+    )
+    trainer.train()
+
+    # fine tuned model metrics
+    ft_metrics = ir_evaluator(model)
+    print(ir_evaluator.primary_metric)
+    print(ft_metrics[ir_evaluator.primary_metric])
+
+
+    metrics = pd.DataFrame([base_metrics, ft_metrics]).T
+    print(metrics)
+    return str(metrics)
+
+
+## logs to UI
+# https://github.com/gradio-app/gradio/issues/2362#issuecomment-1424446778
+
+demo = gr.Interface(fn=greet, inputs=["text", "text", "text", "number", "bool"], outputs=["text"]) # "dataframe"
 demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+datasets
+accelerate
+sentence-transformers