Commit From AutoTrain

.gitattributes

@@ -29,3 +29,5 @@ 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

README.md

@@ -0,0 +1,50 @@
+---
+tags:
+- autotrain
+- text-classification
+language:
+- unk
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- dav3794/autotrain-data-demo-knots-1-2
+co2_eq_emissions:
+  emissions: 0.019866640922183956
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Binary Classification
+- Model ID: 1315950270
+- CO2 Emissions (in grams): 0.0199
+
+## Validation Metrics
+
+- Loss: 0.396
+- Accuracy: 0.792
+- Precision: 0.915
+- Recall: 0.652
+- AUC: 0.900
+- F1: 0.761
+
+## 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/dav3794/autotrain-demo-knots-1-2-1315950270
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("dav3794/autotrain-demo-knots-1-2-1315950270", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("dav3794/autotrain-demo-knots-1-2-1315950270", use_auth_token=True)
+
+inputs = tokenizer("I love AutoTrain", return_tensors="pt")
+
+outputs = model(**inputs)
+```

config.json

@@ -0,0 +1,37 @@
+{
+  "_name_or_path": "AutoTrain",
+  "_num_labels": 2,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "0",
+    "1": "1"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 256,
+  "max_position_embeddings": 40000,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 30,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30
+}

pytorch_model.bin

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+size 1680217005

special_tokens_map.json

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

tokenizer.json

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+{
+  "version": "1.0",
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+    "direction": "Right",
+    "max_length": 256,
+    "strategy": "LongestFirst",
+    "stride": 0
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+      "special": true
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+    "type": "BertNormalizer",
+    "clean_text": true,
+    "handle_chinese_chars": true,
+    "strip_accents": null,
+    "lowercase": false
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+  "pre_tokenizer": {
+    "type": "BertPreTokenizer"
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+    "cleanup": true
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+    }
+  }
+}

tokenizer_config.json

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+{
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+  "do_basic_tokenize": true,
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.txt

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+[PAD]
+[UNK]
+[CLS]
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+[MASK]
+L
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