Text Classification
Transformers
ONNX
Safetensors
bert
social-media
content-filtering
engagement-bait
distillation
text-embeddings-inference
Instructions to use selftaughtdev/engagement-farm-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selftaughtdev/engagement-farm-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selftaughtdev/engagement-farm-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selftaughtdev/engagement-farm-classifier") model = AutoModelForSequenceClassification.from_pretrained("selftaughtdev/engagement-farm-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete folder model_out with huggingface_hub
Browse files- model_out/config.json +0 -33
- model_out/metrics.json +0 -9
- model_out/model.safetensors +0 -3
- model_out/onnx-int8/config.json +0 -33
- model_out/onnx-int8/model_quantized.onnx +0 -3
- model_out/onnx-int8/ort_config.json +0 -33
- model_out/special_tokens_map.json +0 -7
- model_out/tokenizer.json +0 -0
- model_out/tokenizer_config.json +0 -58
- model_out/vocab.txt +0 -0
model_out/config.json
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{
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"dtype": "float32",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 512,
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"id2label": {
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"0": "genuine",
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"1": "engagement_farming"
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},
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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"label2id": {
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"engagement_farming": 1,
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"genuine": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 8,
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"num_hidden_layers": 8,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"transformers_version": "4.57.6",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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model_out/metrics.json
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{
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"eval_loss": 0.17980128526687622,
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"eval_accuracy": 0.972972972972973,
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"eval_f1": 0.9433962264150944,
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"eval_runtime": 2.8042,
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"eval_samples_per_second": 277.086,
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"eval_steps_per_second": 4.636,
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"epoch": 6.0
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}
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model_out/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:426690dca42c7dea1ced3cc6c4055a811220bfcb516bbf002d68167ea58fa94c
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size 165512728
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model_out/onnx-int8/config.json
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{
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"dtype": "float32",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 512,
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"id2label": {
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"0": "genuine",
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"1": "engagement_farming"
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},
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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"label2id": {
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"engagement_farming": 1,
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"genuine": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 8,
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"num_hidden_layers": 8,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"transformers_version": "4.57.6",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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model_out/onnx-int8/model_quantized.onnx
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version https://git-lfs.github.com/spec/v1
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size 42017674
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model_out/onnx-int8/ort_config.json
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{
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"one_external_file": true,
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"opset": null,
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"optimization": {},
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"quantization": {
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"activations_dtype": "QUInt8",
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"activations_symmetric": false,
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"format": "QOperator",
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"is_static": false,
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"mode": "IntegerOps",
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"nodes_to_exclude": [],
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"nodes_to_quantize": [],
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"operators_to_quantize": [
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"Conv",
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"MatMul",
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"Attention",
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"LSTM",
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"Gather",
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"Transpose",
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"EmbedLayerNormalization"
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],
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"per_channel": true,
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"qdq_add_pair_to_weight": false,
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"qdq_dedicated_pair": false,
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"qdq_op_type_per_channel_support_to_axis": {
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"MatMul": 1
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},
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"reduce_range": false,
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"weights_dtype": "QInt8",
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"weights_symmetric": true
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},
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"use_external_data_format": false
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}
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model_out/special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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model_out/tokenizer.json
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model_out/tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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model_out/vocab.txt
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