Upload . with huggingface_hub

README.md

@@ -0,0 +1,50 @@
+---
+tags:
+  - deberta-v3
+inference:
+  parameters:
+    function_to_apply: "none"
+widget:
+- text: "I care only about my own utility. I like dogs. | I cuddled with my dog today."
+---
+# Conditional Utilitarian Deberta 01
+
+## Model description
+
+This is a [Deberta-based](https://huggingface.co/microsoft/deberta-v3-large) model.
+## Intended use
+
+The main use case is the computation of utility estimates of first-person and third-person text scenarios, under extra contextual information. The person's utility to evaluate can be specified in the context.
+
+## Limitations
+
+The model was trained on only ~10000 general utility examples and ~800 conditional utility examples, so it should be expected to have limited performance.
+
+It does not have the capability of interpreting highly complex or unusual scenarios, and it does not have hard guarantees on its domain of accuracy. 
+
+## How to use
+
+Given a scenario S under a context C, and the model U, one computes the estimated conditional utility with `U(f'{C} | {S}') - U(C)`.
+
+In addition, you should specify the person for whom to evaluate utility. The model was trained using the phrases `f"I care only about {person}'s utility."` and `"I care only about my own utility."`.
+
+## Training data
+
+The first training data is the train split from the Utilitarianism part of the [ETHICS dataset](https://arxiv.org/abs/2008.02275).
+
+The second training data consists of ~800 crowdsourced examples of triples (S, C0, C1) consisting of one scenario and two possible contexts, where `U(S | C0) > U(S | C1)`.
+
+Both of these sets are converted from the first person to the third person using GPT3.
+
+## Training procedure
+
+DeBERTa-v3-large was fine-tuned the model over the training data, with a learning rate of `1e-5`, a batch size of `16`, and for 1 epoch.
+
+The training procedure generally follows [tune.py](https://github.com/hendrycks/ethics/blob/3e4c09259a1b4022607da093e9452383fc1bb7e3/utilitarianism/tune.py). In addition to the ranked pairs of both first and third person scenarios, the examples were included to apply the following restrictions:
+
+- First person examples where you care about your own utility and the corresponding third person example where the subject's utility is cared about should have the same utility.
+- Third person examples where you care about your own utility and first person examples where you care about a random person's utility (not in the scenario) should each have zero utility.
+
+## Evaluation results
+
+The model achieves ~80% accuracy over the ethics test set, from the same distribution as the training data.

added_tokens.json

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

config.json

@@ -0,0 +1,41 @@
+{
+  "_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"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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": "4.24.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66531f5a7258eb1a1e539737ab35eddf82d82afd8fd5b92d7ca20510019f387b
+size 1740389291

special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

spm.model

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

tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "name_or_path": "microsoft/deberta-v3-large",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "special_tokens_map_file": null,
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}