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 +1,56 @@
+---
+tags:
+- autotrain
+- text-classification
+language:
+- unk
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- AyoubChLin/autotrain-data-albert-bbc-news
+co2_eq_emissions:
+  emissions: 13.344689233410659
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 48939118438
+- CO2 Emissions (in grams): 13.3447
+
+## Validation Metrics
+
+- Loss: 0.103
+- Accuracy: 0.978
+- Macro F1: 0.978
+- Micro F1: 0.978
+- Weighted F1: 0.978
+- Macro Precision: 0.977
+- Micro Precision: 0.978
+- Weighted Precision: 0.978
+- Macro Recall: 0.978
+- Micro Recall: 0.978
+- Weighted Recall: 0.978
+
+
+## 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/AyoubChLin/autotrain-albert-bbc-news-48939118438
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/autotrain-albert-bbc-news-48939118438", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/autotrain-albert-bbc-news-48939118438", 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": 5,
+  "architectures": [
+    "AlbertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "gap_size": 0,
+  "hidden_act": "gelu_new",
+  "hidden_dropout_prob": 0,
+  "hidden_size": 4096,
+  "id2label": {
+    "0": "0",
+    "1": "1",
+    "2": "2",
+    "3": "3",
+    "4": "4"
+  },
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 16384,
+  "label2id": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "layers_to_keep": [],
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 64,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 12,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 2,
+  "vocab_size": 30000
+}

pytorch_model.bin

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special_tokens_map.json

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+  "mask_token": {
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+}

spiece.model

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tokenizer.json

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tokenizer_config.json

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+  "tokenizer_class": "AlbertTokenizer",
+  "unk_token": "<unk>"
+}