Upload 4 files

backup/model.py

@@ -5,11 +5,11 @@ import re
 from typing import Dict, Optional, Union
 import torch
 import torch.nn.functional as F
-from .modules.layers import LstmSeq2SeqEncoder
-from .modules.base import InstructBase
-from .modules.evaluator import Evaluator, greedy_search
-from .modules.span_rep import SpanRepLayer
-from .modules.token_rep import TokenRepLayer
+from modules.layers import LstmSeq2SeqEncoder
+from modules.base import InstructBase
+from modules.evaluator import Evaluator, greedy_search
+from modules.span_rep import SpanRepLayer
+from modules.token_rep import TokenRepLayer
 from torch import nn
 from torch.nn.utils.rnn import pad_sequence
 from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
@@ -320,7 +320,7 @@ class GLiNER(InstructBase, PyTorchModelHubMixin):
             return model
 
         # 2. Newer format: Use "pytorch_model.bin" and "gliner_config.json"
-        from .train import load_config_as_namespace
+        from train import load_config_as_namespace
 
         model_file = Path(model_id) / "pytorch_model.bin"
         if not model_file.exists():

backup/save_load.py

@@ -1,5 +1,5 @@
 import torch
-from .model import GLiNER
+from model import GLiNER
 
 
 def save_model(current_model, path):

backup/train.py

@@ -7,9 +7,9 @@ from tqdm import tqdm
 from transformers import get_cosine_schedule_with_warmup
 
 # from model_nested import NerFilteredSemiCRF
-from .model import GLiNER
-from .modules.run_evaluation import get_for_all_path, sample_train_data
-from .save_load import save_model, load_model
+from model import GLiNER
+from modules.run_evaluation import get_for_all_path, sample_train_data
+from save_load import save_model, load_model
 import json