feat(*): create a new model from deberta-v3-small

Using the `microsoft/deberta-v3-small` model and a dataset titled
`us-patent-phrase-to-phrase-matching`, train a new model that can
perform NLP on the US Patent phrases, classifying anchors and targets
against a score.

All of this was done in the context of the "Practical Deep Learning for
Coders 2022" YouTube course and its fourth lesson. It can be accessed by
following this URL: https://youtu.be/toUgBQv1BT8

added_tokens.json

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+}

config.json

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+  "_name_or_path": "microsoft/deberta-v3-small",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
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+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
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+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
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+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
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+  "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

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special_tokens_map.json

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tokenizer_config.json

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+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "name_or_path": "microsoft/deberta-v3-small",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
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+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

training_args.bin

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