feat: eval for retrieval

configs/eval/evaluate_gpt4all_jr.yaml

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+# model/tokenizer
+model_name: "/home/paperspace/gpt4all/gpt4all/train/ckpts/epoch_2"
+tokenizer_name: "EleutherAI/gpt-j-6B"
+version: null
+gradient_checkpointing: true
+save_name: "nomic-ai/gpt-jr"
+encoder_dim: 384
+
+# dataset
+streaming: false
+num_proc: 64
+dataset_path: "/home/paperspace/gpt4all/gpt4all/index/squad_supplemented_validation" 
+max_length: 1024
+batch_size: 32
+pct_test: 0.05
+q_column: "question"
+a_column: "answers"
+encoder_column: "neighbor_embeddings"

gpt4all/eval/eval_squad.py

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+import torch
+from gpt4all.models import GPTJRForCausalLM
+from gpt4all.data.retrieval_dataloader import load_retrieval_augmented_data
+from gpt4all.train.metrics import f1_score, exact_match_score
+from gpt4all.utils.read import read_config
+from transformers import AutoTokenizer
+from argparse import ArgumentParser
+from tqdm import tqdm
+
+parser = ArgumentParser()
+parser.add_argument("--config", type=str, default="config.yaml")
+
+args = parser.parse_args()
+
+config = read_config(args.config)
+
+tokenizer = AutoTokenizer.from_pretrained(config["tokenizer_name"])
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+dataloader = load_retrieval_augmented_data(config, tokenizer, split_dataset=False)
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = GPTJRForCausalLM.from_pretrained(config["model_name"], use_cache=False)
+model.to(device)
+model.eval()
+
+# Evaluate the model on the SQUAD dataset
+f1s = []
+exact_matches = []
+with torch.no_grad():
+    for batch in tqdm(dataloader):
+        outputs = model(input_ids=batch["input_ids"].to(device),
+                        labels=batch["labels"].to(device),
+                        encoder_hidden_states=batch["encoder_hidden_states"].to(device))
+
+        predicted_tokens = outputs.logits.argmax(dim=-1)
+        predicted = tokenizer.batch_decode(predicted_tokens, skip_special_tokens=True)
+
+        labels = batch["labels"]
+        mask = labels == -100
+        labels[mask] = tokenizer.pad_token_id
+        ground_truth = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        f1 = f1_score(predicted, ground_truth)
+        exact_match = exact_match_score(predicted, ground_truth)
+
+        f1s.extend(f1)
+        exact_matches.extend(exact_match)
+
+
+print(torch.tensor(f1s).mean())
+print(torch.tensor(exact_matches).to(torch.float32).mean())