First upload.

README.md

@@ -1,3 +1,57 @@
 ---
+language: en
+tags:
+  - deberta
+  - deberta-v3
 license: mit
 ---
+
+# Cross Talk
+
+## Multiturn Conversation by Reranking.
+
+Cross Talk is a [DeBERTa V3 Large](https://huggingface.co/microsoft/deberta-v3-large) finetune designed to identify which outgoing text candidates best follow conversational history. It is trained on a "next-like, not next-like" corpus derived from Open Subtitles. It was intended for _recreational use._
+
+## Concept
+
+Suppose we have the following exchange in a conversational history:
+
+```
+[user] I'm out of coffee.
+[bot] What will you do about it?
+[user] I guess I'll buy more.
+```
+
+And by some generative model, we have three candidate texts:
+
+1. `Buy more what?`
+2. `Is it expensive?`
+3. `Colorless green ideas sleep furiously.`
+
+We should be able to score item 2 as the most ideal, and item 3 as the least ideal.
+
+## Motivation
+
+This is a product of [an experiment](https://joecooper.me/blog/crosstalk/) to
+run multiturn conversational AI entirely by reranking of candidates.
+
+In the experiment, a Markov text generator produces wholly random candidates
+with no regard to user input. These candidates are then scored by the model.
+The trained model acts exclusively as a judge.
+
+## Input / Output
+
+Elements are scored pointwise; the model should identify whether a given
+candidate in isolation fits the context. To this end, the model is trained
+on context + candidate pairs. The context is presented as series of lines,
+concatenated together using a `|` pipe (token id `1540`), like so:
+
+```
+[cls] line | line | … [sep] candidate [sep] [pad] …
+```
+
+Output is a single logit.
+
+## Training
+
+Model was trained for 40 hours on a single Nvidia 3090, on 130m tokens of content from Open Subtitles with some pruning and processing of the data. The model scores 92% on the test set, derived from content unrelated to the training set.

config.json

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+{
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "dtype": "float32",
+  "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,
+  "legacy": true,
+  "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,
+  "problem_type": "single_label_classification",
+  "relative_attention": true,
+  "share_att_key": true,
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

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+size 1740300340

spm.model

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+size 2464616

tokenizer_config.json

@@ -0,0 +1,4 @@
+{
+  "do_lower_case": false,
+  "vocab_type": "spm"
+}