Commit From AutoTrain

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,45 @@
+---
+tags: autotrain
+language: en
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- Shenzy2/autotrain-data-NER4DesignTutor
+co2_eq_emissions: 0.004032656988228696
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Entity Extraction
+- Model ID: 1169643336
+- CO2 Emissions (in grams): 0.004032656988228696
+
+## Validation Metrics
+
+- Loss: 0.677674412727356
+- Accuracy: 0.8129095674967235
+- Precision: 0.4424778761061947
+- Recall: 0.4844961240310077
+- F1: 0.4625346901017577
+
+## Usage
+
+You can use cURL to access this model:
+
+```
+$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Shenzy2/autotrain-NER4DesignTutor-1169643336
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model = AutoModelForTokenClassification.from_pretrained("Shenzy2/autotrain-NER4DesignTutor-1169643336", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("Shenzy2/autotrain-NER4DesignTutor-1169643336", use_auth_token=True)
+
+inputs = tokenizer("I love AutoTrain", return_tensors="pt")
+
+outputs = model(**inputs)
+```

config.json

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+{
+  "_name_or_path": "AutoTrain",
+  "_num_labels": 7,
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "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": "B-Nolabel",
+    "1": "B-design_concept",
+    "2": "B-ui_element",
+    "3": "I-Nolabel",
+    "4": "I-design_concept",
+    "5": "I-ui_element",
+    "6": "O"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-Nolabel": 0,
+    "B-design_concept": 1,
+    "B-ui_element": 2,
+    "I-Nolabel": 3,
+    "I-design_concept": 4,
+    "I-ui_element": 5,
+    "O": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 96,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

pytorch_model.bin

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+oid sha256:2e74f922c7afff731370d3eecf4ccf38a5310d3bdf529d261af2a2f130740786
+size 430970673

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "AutoTrain",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}