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Browse files- README.md +50 -0
- config.json +51 -0
- pytorch_model.bin +3 -0
    	
        README.md
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            ---
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            tags: autonlp
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            language: bn
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            widget:
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            - text: "I love AutoNLP 🤗"
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            datasets:
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            - albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664
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            ---
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            # Model Trained Using AutoNLP
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            - Problem type: Multi-class Classification
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            - Model ID: 1311135
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            ## Validation Metrics
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            - Loss: 0.35616958141326904
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            - Accuracy: 0.8979447200566973
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            - Macro F1: 0.8545383956197669
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            - Micro F1: 0.8979447200566975
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            - Weighted F1: 0.8983951947775538
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            - Macro Precision: 0.8615833774439791
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            - Micro Precision: 0.8979447200566973
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            - Weighted Precision: 0.9013559365881655
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            - Macro Recall: 0.8516503001777104
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            - Micro Recall: 0.8979447200566973
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            - Weighted Recall: 0.8979447200566973
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            ## Usage
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            You can use cURL to access this model:
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            ```
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            $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135
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            ```
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            Or Python API:
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            ```
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            from transformers import AutoModelForSequenceClassification, AutoTokenizer
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            model = AutoModelForSequenceClassification.from_pretrained("albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135", use_auth_token=True)
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            tokenizer = AutoTokenizer.from_pretrained("albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135", use_auth_token=True)
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            inputs = tokenizer("I love AutoNLP", return_tensors="pt")
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            outputs = model(**inputs)
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            ```
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        config.json
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            {
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              "_name_or_path": "AutoNLP",
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              "_num_labels": 6,
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              "architectures": [
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                "AlbertForSequenceClassification"
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              ],
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              "attention_probs_dropout_prob": 0,
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              "bos_token_id": 2,
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              "classifier_dropout_prob": 0.1,
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              "down_scale_factor": 1,
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              "embedding_size": 128,
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              "eos_token_id": 3,
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              "gap_size": 0,
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              "hidden_act": "gelu_new",
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              "hidden_dropout_prob": 0,
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              "hidden_size": 1024,
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              "id2label": {
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                "0": "0",
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                "1": "1",
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                "2": "2",
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                "3": "3",
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                "4": "4",
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                "5": "5"
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              },
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              "initializer_range": 0.02,
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              "inner_group_num": 1,
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              "intermediate_size": 4096,
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              "label2id": {
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                "0": 0,
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                "1": 1,
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                "2": 2,
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                "3": 3,
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                "4": 4,
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                "5": 5
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              },
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              "layer_norm_eps": 1e-12,
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              "max_length": 128,
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              "max_position_embeddings": 512,
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              "model_type": "albert",
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              "net_structure_type": 0,
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              "num_attention_heads": 16,
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              "num_hidden_groups": 1,
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              "num_hidden_layers": 24,
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              "num_memory_blocks": 0,
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              "pad_token_id": 0,
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              "padding": "max_length",
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              "position_embedding_type": "absolute",
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              "transformers_version": "4.5.1",
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              "type_vocab_size": 2,
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              "vocab_size": 32000
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            }
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        pytorch_model.bin
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            version https://git-lfs.github.com/spec/v1
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            oid sha256:fe3029ca82ec1d261846f925daf36715fdad72d55266a2ca79dc46f5f77797ce
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            size 71800683
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