Token Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use cfilt/HiNER-collapsed-xlm-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cfilt/HiNER-collapsed-xlm-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cfilt/HiNER-collapsed-xlm-roberta-large")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cfilt/HiNER-collapsed-xlm-roberta-large") model = AutoModelForTokenClassification.from_pretrained("cfilt/HiNER-collapsed-xlm-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
d946564
1
Parent(s): 038013a
End of training
Browse files- .gitignore +1 -0
- GOAT +1 -0
- config.json +46 -0
- dev_predictions.txt +0 -0
- dev_results.txt +7 -0
- pytorch_model.bin +3 -0
- runs/May01_02-46-14_cccxc523/events.out.tfevents.1651388621.cccxc523.1363939.0 +3 -0
- test_predictions.txt +0 -0
- test_results.txt +7 -0
- training_args.bin +3 -0
.gitignore
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checkpoint-*/
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GOAT
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Subproject commit 038013ad57782e56ce6cd591854813743f45a960
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config.json
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{
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"_name_or_path": "../HiNER/cfilt_collapsed/cfilt_collpased-xlm-roberta-large_ner_32_5e-05_10_1/",
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "B-LOCATION",
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"1": "B-ORGANIZATION",
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"2": "B-PERSON",
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"3": "I-LOCATION",
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"4": "I-ORGANIZATION",
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"5": "I-PERSON",
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"6": "O"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"B-LOCATION": 0,
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"B-ORGANIZATION": 1,
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"B-PERSON": 2,
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"I-LOCATION": 3,
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"I-ORGANIZATION": 4,
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"I-PERSON": 5,
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"O": 6
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.14.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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}
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dev_predictions.txt
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dev_results.txt
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test_loss = 0.06026517227292061
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test_precision = 0.9174308520512344
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test_recall = 0.9319278925931513
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test_f1 = 0.9246225514957624
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test_runtime = 181.2481
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test_samples_per_second = 60.221
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test_steps_per_second = 7.531
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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:b485917b09341554c6ac9252f423797663f65b09d635ae9d80e5b89b38dfafc5
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size 2235555441
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runs/May01_02-46-14_cccxc523/events.out.tfevents.1651388621.cccxc523.1363939.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:b890683480255bfcb98cb39801b6fa3a2d4672f022226503ac1b8a756f77dd90
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size 389
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test_predictions.txt
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test_results.txt
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test_loss = 0.05991547554731369
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test_precision = 0.9137448834064936
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test_recall = 0.9296549644788663
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test_f1 = 0.9216312652954473
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test_runtime = 356.0808
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test_samples_per_second = 61.306
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test_steps_per_second = 7.664
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:6c61de6bae3455fcd9af664ca8658a6d3b064962537db5313598eecca341ec83
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size 3055
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