G-ML-Hyly/gls

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: cdp-gls
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# cdp-gls
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.0808
+- Accuracy: 0.4839
+
+## 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:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 3.4949        | 1.0   | 10   | 3.4712          | 0.0645   |
+| 3.4362        | 2.0   | 20   | 3.4293          | 0.1452   |
+| 3.3557        | 3.0   | 30   | 3.3663          | 0.2581   |
+| 3.2469        | 4.0   | 40   | 3.3081          | 0.3710   |
+| 3.1742        | 5.0   | 50   | 3.2495          | 0.3871   |
+| 3.0934        | 6.0   | 60   | 3.1932          | 0.4032   |
+| 3.0142        | 7.0   | 70   | 3.1443          | 0.4516   |
+| 2.9634        | 8.0   | 80   | 3.1101          | 0.4516   |
+| 2.9353        | 9.0   | 90   | 3.0889          | 0.4839   |
+| 2.9034        | 10.0  | 100  | 3.0808          | 0.4839   |
+
+
+### Framework versions
+
+- Transformers 4.38.0.dev0
+- Pytorch 2.1.2+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "all dimensions",
+    "1": "all dimensions in crstal by assist",
+    "2": "all dimensions in crstal by medium",
+    "3": "all dimensions in crstal by source",
+    "4": "all metrics",
+    "5": "applied by assist",
+    "6": "applied by medium",
+    "7": "applied by source",
+    "8": "assist",
+    "9": "created leads by assist",
+    "10": "created leads by medium",
+    "11": "created leads by source",
+    "12": "crstal by assist",
+    "13": "crstal by medium",
+    "14": "crstal by source",
+    "15": "crstal milestones",
+    "16": "date range",
+    "17": "direct source",
+    "18": "lead gen analysis",
+    "19": "leased by assist",
+    "20": "leased by medium",
+    "21": "leased by source",
+    "22": "medium",
+    "23": "property name",
+    "24": "prospect journey",
+    "25": "source",
+    "26": "toured by assist",
+    "27": "toured by medium",
+    "28": "toured by source",
+    "29": "tours scheduled by assist",
+    "30": "tours scheduled by medium",
+    "31": "tours scheduled by source",
+    "32": "unspecified source"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "all dimensions": 0,
+    "all dimensions in crstal by assist": 1,
+    "all dimensions in crstal by medium": 2,
+    "all dimensions in crstal by source": 3,
+    "all metrics": 4,
+    "applied by assist": 5,
+    "applied by medium": 6,
+    "applied by source": 7,
+    "assist": 8,
+    "created leads by assist": 9,
+    "created leads by medium": 10,
+    "created leads by source": 11,
+    "crstal by assist": 12,
+    "crstal by medium": 13,
+    "crstal by source": 14,
+    "crstal milestones": 15,
+    "date range": 16,
+    "direct source": 17,
+    "lead gen analysis": 18,
+    "leased by assist": 19,
+    "leased by medium": 20,
+    "leased by source": 21,
+    "medium": 22,
+    "property name": 23,
+    "prospect journey": 24,
+    "source": 25,
+    "toured by assist": 26,
+    "toured by medium": 27,
+    "toured by source": 28,
+    "tours scheduled by assist": 29,
+    "tours scheduled by medium": 30,
+    "tours scheduled by source": 31,
+    "unspecified source": 32
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.0.dev0",
+  "vocab_size": 30522
+}

model.safetensors

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special_tokens_map.json

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+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
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tokenizer_config.json

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+  "clean_up_tokenization_spaces": true,
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+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

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