Delete models/retriever/level-4-small-no-negative-outcomes

models/retriever/level-4-small-no-negative-outcomes/document_index/config.yaml

@@ -1,5 +0,0 @@
-_target_: relik.retriever.indexers.inmemory.InMemoryDocumentIndex
-metadata_fields:
-- definition
-separator: ' <def> '
-name_or_path: null

models/retriever/level-4-small-no-negative-outcomes/document_index/embeddings.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f48d0753bec8ae343e33355d98538895384ee9115d421e625f22420164a7c5bf
-size 1352875

models/retriever/level-4-small-no-negative-outcomes/document_index_db/config.yaml

@@ -1,5 +0,0 @@
-_target_: relik.retriever.indexers.inmemory.InMemoryDocumentIndex
-metadata_fields:
-- definition
-separator: ' <def> '
-name_or_path: null

models/retriever/level-4-small-no-negative-outcomes/document_index_db/documents.jsonl

@@ -1,2 +0,0 @@
-{"text": "financial literacy", "id": 0, "metadata": {"definition": "outcome. measures of skills or knowledge related to finance and reporting.", "level": 4, "type": "outcome"}}
-{"text": "secondary water treatments", "id": 1, "metadata": {"definition": "outcome. use of secondary/back-up forms of water treatment and supply.", "level": 4, "type": "outcome"}}

models/retriever/level-4-small-no-negative-outcomes/document_index_db/embeddings.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f38d784feae82d94bd56010427349604de367cf1d7547a0d6720070878e9bec3
-size 4267

models/retriever/level-4-small-no-negative-outcomes/question_encoder/config.json

@@ -1,29 +0,0 @@
-{
-  "_name_or_path": "intfloat/e5-small-v2",
-  "architectures": [
-    "GoldenRetrieverModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "auto_map": {
-    "AutoModel": "hf.GoldenRetrieverModel"
-  },
-  "classifier_dropout": null,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 384,
-  "initializer_range": 0.02,
-  "intermediate_size": 1536,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "projection_dim": null,
-  "torch_dtype": "float32",
-  "transformers_version": "4.33.3",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
-}

models/retriever/level-4-small-no-negative-outcomes/question_encoder/hf.py

@@ -1,99 +0,0 @@
-from typing import Tuple, Union
-
-import torch
-from transformers import PretrainedConfig
-from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
-from transformers.models.bert.modeling_bert import BertModel
-
-
-class GoldenRetrieverConfig(PretrainedConfig):
-    model_type = "bert"
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
-        type_vocab_size=2,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        pad_token_id=0,
-        position_embedding_type="absolute",
-        use_cache=True,
-        classifier_dropout=None,
-        projection_dim=None,
-        **kwargs,
-    ):
-        super().__init__(pad_token_id=pad_token_id, **kwargs)
-
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.intermediate_size = intermediate_size
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.type_vocab_size = type_vocab_size
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.position_embedding_type = position_embedding_type
-        self.use_cache = use_cache
-        self.classifier_dropout = classifier_dropout
-        self.projection_dim = projection_dim
-
-
-class GoldenRetrieverModel(BertModel):
-    config_class = GoldenRetrieverConfig
-
-    def __init__(self, config, *args, **kwargs):
-        super().__init__(config)
-        self.layer_norm_layer = torch.nn.LayerNorm(
-            config.hidden_size, eps=config.layer_norm_eps
-        )
-        self.projection: torch.nn.Module | None = None
-        if config.projection_dim is not None:
-            self.projection = torch.nn.Sequential(
-                torch.nn.Linear(config.hidden_size, config.projection_dim),
-                torch.nn.LayerNorm(config.projection_dim),
-            )
-
-    def forward(
-        self, **kwargs
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
-        attention_mask = kwargs.get("attention_mask", None)
-        model_outputs = super().forward(**kwargs)
-        if attention_mask is None:
-            pooler_output = model_outputs.pooler_output
-        else:
-            token_embeddings = model_outputs.last_hidden_state
-            input_mask_expanded = (
-                attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-            )
-            pooler_output = torch.sum(
-                token_embeddings * input_mask_expanded, 1
-            ) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-        pooler_output = self.layer_norm_layer(pooler_output)
-
-        if self.projection is not None:
-            pooler_output = self.projection(pooler_output)
-
-        if not kwargs.get("return_dict", True):
-            return (model_outputs[0], pooler_output) + model_outputs[2:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=model_outputs.last_hidden_state,
-            pooler_output=pooler_output,
-            past_key_values=model_outputs.past_key_values,
-            hidden_states=model_outputs.hidden_states,
-            attentions=model_outputs.attentions,
-            cross_attentions=model_outputs.cross_attentions,
-        )

models/retriever/level-4-small-no-negative-outcomes/question_encoder/pytorch_model.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a2ff7f63276ef10722db26f66a406bc4aa6f5a345dd177d243cb826bb7db48b7
-size 133510894

models/retriever/level-4-small-no-negative-outcomes/question_encoder/special_tokens_map.json

@@ -1,7 +0,0 @@
-{
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}

models/retriever/level-4-small-no-negative-outcomes/question_encoder/tokenizer_config.json

@@ -1,13 +0,0 @@
-{
-  "clean_up_tokenization_spaces": true,
-  "cls_token": "[CLS]",
-  "do_lower_case": true,
-  "mask_token": "[MASK]",
-  "model_max_length": 512,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "unk_token": "[UNK]"
-}