Upload TFDistilBertForSequenceClassification

README.md

@@ -1,3 +1,46 @@
----
-license: apache-2.0
----
+---
+tags:
+- generated_from_keras_callback
+model-index:
+- name: industry-classification
+  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. -->
+
+# industry-classification
+
+This model was trained from scratch on an unknown dataset.
+It achieves the following results on the evaluation set:
+
+
+## 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: None
+- training_precision: float32
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- TensorFlow 2.13.0
+- Tokenizers 0.13.3

config.json

@@ -1,24 +1,67 @@
 {
+  "_name_or_path": "models/model_industry",
+  "activation": "gelu",
   "architectures": [
-    "BertModel"
+    "DistilBertForSequenceClassification"
   ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "Accomodation Services",
+    "1": "Administrative and Support Services",
+    "2": "Construction",
+    "3": "Consumer Services",
+    "4": "Education",
+    "5": "Entertainment Providers",
+    "6": "Farming, Ranching, Forestry",
+    "7": "Financial Services",
+    "8": "Government Administration",
+    "9": "Holding Companies",
+    "10": "Hospitals and Health Care",
+    "11": "Manufacturing",
+    "12": "Oil, Gas, and Mining",
+    "13": "Professional Services",
+    "14": "Real Estate and Equipment Rental Services",
+    "15": "Retail",
+    "16": "Technology, Information and Media",
+    "17": "Transportation, Logistics and Storage",
+    "18": "Utilities",
+    "19": "Wholesale"
+  },
   "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-12,
+  "label2id": {
+    "Accomodation Services": 0,
+    "Administrative and Support Services": 1,
+    "Construction": 2,
+    "Consumer Services": 3,
+    "Education": 4,
+    "Entertainment Providers": 5,
+    "Farming, Ranching, Forestry": 6,
+    "Financial Services": 7,
+    "Government Administration": 8,
+    "Holding Companies": 9,
+    "Hospitals and Health Care": 10,
+    "Manufacturing": 11,
+    "Oil, Gas, and Mining": 12,
+    "Professional Services": 13,
+    "Real Estate and Equipment Rental Services": 14,
+    "Retail": 15,
+    "Technology, Information and Media": 16,
+    "Transportation, Logistics and Storage": 17,
+    "Utilities": 18,
+    "Wholesale": 19
+  },
   "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
   "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
   "transformers_version": "4.31.0",
-  "type_vocab_size": 2,
-  "use_cache": true,
   "vocab_size": 30522
 }

tf_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de2d0bee594dd31c6792b4cdf230c173845e8119381ab6952af3015a2d723f05
+size 268010440