add model

README.md

@@ -1,3 +1,55 @@
 ---
-license: afl-3.0
+tags:
+- generated_from_keras_callback
+model-index:
+- name: CAP_coded_US_Congressional_bills
+  results: []
 ---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# CAP_coded_US_Congressional_bills
+
+This model was trained from scratch on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 0.0852
+- Train Sparse Categorical Accuracy: 0.9793
+- Validation Loss: 0.6009
+- Validation Sparse Categorical Accuracy: 0.8340
+- Epoch: 2
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
+|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
+| 0.5269     | 0.8476                            | 0.4876          | 0.8540                                 | 0     |
+| 0.1683     | 0.9545                            | 0.5160          | 0.8520                                 | 1     |
+| 0.0852     | 0.9793                            | 0.6009          | 0.8340                                 | 2     |
+
+
+### Framework versions
+
+- Transformers 4.19.3
+- TensorFlow 2.8.2
+- Tokenizers 0.12.1

config.json

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+{
+  "_name_or_path": "/content/drive/MyDrive/NLP_models/US_60K_normalised-bert-cased.t5",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "macroeconomics",
+    "1": "civil rights",
+    "2": "health",
+    "3": "agriculture",
+    "4": "labor",
+    "5": "education",
+    "6": "environment",
+    "7": "energy",
+    "8": "immigration",
+    "9": "transportation",
+    "10": "law and crime",
+    "11": "social welfare",
+    "12": "housing",
+    "13": "domestic commerce",
+    "14": "defense",
+    "15": "technology",
+    "16": "foreign trade",
+    "17": "international affairs",
+    "18": "government operations",
+    "19": "public lands"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_12": 12,
+    "LABEL_13": 13,
+    "LABEL_14": 14,
+    "LABEL_15": 15,
+    "LABEL_16": 16,
+    "LABEL_17": 17,
+    "LABEL_18": 18,
+    "LABEL_19": 19,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "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",
+  "transformers_version": "4.19.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

tf_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fd0b5c2cdfa67749fb52337fe9c91983876b6670aa63395064d800cfd44b3ebf
+size 433590688