Commit From AutoTrain

.gitattributes

@@ -32,3 +32,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,49 @@
+---
+tags:
+- autotrain
+- token-classification
+language:
+- unk
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- sxandie/autotrain-data-re_syn_cleanedtext_bert
+co2_eq_emissions:
+  emissions: 0.0038016334989771365
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Entity Extraction
+- Model ID: 55272128958
+- CO2 Emissions (in grams): 0.0038
+
+## Validation Metrics
+
+- Loss: 0.192
+- Accuracy: 0.959
+- Precision: 0.651
+- Recall: 0.686
+- F1: 0.668
+
+## 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/sxandie/autotrain-re_syn_cleanedtext_bert-55272128958
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model = AutoModelForTokenClassification.from_pretrained("sxandie/autotrain-re_syn_cleanedtext_bert-55272128958", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("sxandie/autotrain-re_syn_cleanedtext_bert-55272128958", use_auth_token=True)
+
+inputs = tokenizer("I love AutoTrain", return_tensors="pt")
+
+outputs = model(**inputs)
+```

config.json

@@ -0,0 +1,74 @@
+{
+  "_name_or_path": "AutoTrain",
+  "_num_labels": 18,
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "commodity",
+    "2": "company",
+    "3": "delivery_cap",
+    "4": "delivery_location",
+    "5": "delivery_port",
+    "6": "delivery_state",
+    "7": "incoterms",
+    "8": "measures",
+    "9": "package_type",
+    "10": "pickup_cap",
+    "11": "pickup_location",
+    "12": "pickup_port",
+    "13": "pickup_state",
+    "14": "quantity",
+    "15": "stackable",
+    "16": "volume",
+    "17": "weight"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "O": 0,
+    "commodity": 1,
+    "company": 2,
+    "delivery_cap": 3,
+    "delivery_location": 4,
+    "delivery_port": 5,
+    "delivery_state": 6,
+    "incoterms": 7,
+    "measures": 8,
+    "package_type": 9,
+    "pickup_cap": 10,
+    "pickup_location": 11,
+    "pickup_port": 12,
+    "pickup_state": 13,
+    "quantity": 14,
+    "stackable": 15,
+    "volume": 16,
+    "weight": 17
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

pytorch_model.bin

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special_tokens_map.json

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

tokenizer.json

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tokenizer_config.json

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