initial

README.md

@@ -0,0 +1,23 @@
+---
+model-index:
+- name: jiazhengli/deberta-v3-large-Rationale-to-Score
+  results: []
+language:
+- en
+base_model: microsoft/deberta-v3-large
+license: apache-2.0
+widget:
+- text: "The student's answer received a score of 0 according to the marking rubric, as it failed to describe any additional pieces of information necessary to accurately replicate the experiment. The key answer elements required specific details such as the amount and type of vinegar used, the materials tested, the size\/surface area of materials, the rinsing and drying durations, the drying method, and the size\/type of container. The student's response did not address any of these points; instead, it suggested changes to the experimental conditions like 'there can be a different amount of time','more containers', 'different placements', and 'temperature differences', which do not provide the specific information needed for replication. "
+  example_title: "Example 1"
+- text: "The student's answer scored 3 points as per the marking rubric, which requires drawing a valid conclusion supported by data and describing two ways to improve the experimental design. The student correctly concluded that 'plastic type B, is the most effective type of plastic' matching the key answer element for a valid experimental conclusion. The student also suggested additional trials ('In the experiment, the group could have done 1 more trial to really reinforce the results') and maintaining weight constant ('the group also should have measured the weight of the weights, and kept the weight constant for more valid results'), aligning with the key answer elements for improving experimental design. However, the answer did not suggest ensuring uniform initial measurements or sample thickness, which were other key answer elements. "
+  example_title: "Example 2"
+- text: "The student's answer scored 0 points according to the marking rubric, which awards 3 points for addressing three key elements, 2 points for two, 1 point for one, and 0 points for none. The student's response failed to address any of the key answer elements accurately. Specifically, the student mentioned 'Equillibrium' and 'Diffusion' but did not explain these processes in the context of cell membrane transport mechanisms like selective permeability, passive transport, osmosis, facilitated diffusion, active transport, or the use of pumps and protein channels. Additionally, there was no mention of membrane-assisted processes such as exocytosis, endocytosis, phagocytosis, or pinocytosis. "
+  example_title: "Example 3"
+- text: "The student's answer scored 1 point based on the marking rubric that awards one point for addressing 'One or two key elements'. The student's response correctly addressed the process of mRNA traveling to the ribosomes as evidenced by 'First it travels along to the ribosomes', fulfilling one key answer element. However, the response failed to correctly describe the interactions and processes involving codons, anticodons, and the specific mechanism of tRNA in protein synthesis, as well as the initiation and elongation phases of protein synthesis, which are vital for higher scoring. The phrases 'the ribosomes give the mRNA its proper tRNA' and 'Then, it is sent off' show confusion about tRNA's role and incorrect understanding of mRNA's interaction with ribosomes. "
+  example_title: "Example 4"
+---
+
+# Model Card for deberta-v3-large-Rationale-to-Score
+
+This repository provides a fine-tuned version of microsoft/deberta-v3-large,
+

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "[MASK]": 128000
+}

config.json

@@ -0,0 +1,51 @@
+{
+  "_name_or_path": "microsoft/deberta-v3-large",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:182bee46372906150391ce603009d49303e510fd134239b030477e32a4d60c1b
+size 1740320848

rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70fb7f0ff800426b3f99f3430d33a22695a2e3c6a02964c9af5cbd09cb26f11a
+size 14244

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

spm.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128000": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0523684dd4d2b31b33fe5afac99de26c74358181242e36ff176b3d05ecd32676
+size 4984