Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use johntang/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johntang/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="johntang/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("johntang/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("johntang/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- config.json +1 -11
- pytorch_model.bin +1 -1
- tokenizer_config.json +1 -3
- training_args.bin +1 -1
config.json
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@@ -1,5 +1,5 @@
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{
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"_name_or_path": "distilbert-base-uncased
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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@@ -7,22 +7,12 @@
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"finetuning_task": "sst-2",
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"hidden_dim": 3072,
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"id2label": {
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"0": "NEGATIVE",
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"1": "POSITIVE"
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},
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"initializer_range": 0.02,
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"label2id": {
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"NEGATIVE": 0,
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"POSITIVE": 1
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"output_past": true,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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{
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"_name_or_path": "distilbert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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pytorch_model.bin
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 267854321
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version https://git-lfs.github.com/spec/v1
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oid sha256:b0edd6ce4b41c32bc02ebc1d2b2714eaa238bd8a9863b41bae0b61519ed73004
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size 267854321
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tokenizer_config.json
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@@ -1,11 +1,9 @@
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{
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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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"mask_token": "[MASK]",
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"model_max_length": 512,
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"name_or_path": "distilbert-base-uncased
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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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"special_tokens_map_file": null,
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{
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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+
"name_or_path": "distilbert-base-uncased",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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training_args.bin
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 3311
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version https://git-lfs.github.com/spec/v1
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oid sha256:8ee423264dfe12960bbfbb1af259dbc2539efa633cc9f61e7cc447d20722fbfe
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size 3311
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