Instructions to use tner/roberta-large-tweetner7-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/roberta-large-tweetner7-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/roberta-large-tweetner7-random")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/roberta-large-tweetner7-random") model = AutoModelForTokenClassification.from_pretrained("tner/roberta-large-tweetner7-random") - Notebooks
- Google Colab
- Kaggle
model update
Browse files
README.md
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name: Token Classification
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type: token-classification
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dataset:
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name: tner/tweetner7
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type: tner/tweetner7
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args: tner/tweetner7
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metrics:
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- name: F1
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type: f1
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value: 0.6632769652650823
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- name: Precision
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type: precision
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value: 0.6554878048780488
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- name: Recall
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type: recall
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value: 0.6712534690101758
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- name: F1 (
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type: f1_macro
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value: 0.6096477771855761
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- name: Precision (
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type: precision_macro
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value: 0.6042443991246051
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- name: Recall (
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type: recall_macro
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value: 0.6191008735553379
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- name: F1 (
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type: f1_entity_span
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value: 0.7900359938296291
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- name: Precision (
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type: precision_entity_span
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value: 0.780713640469738
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- name: Recall (
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type: recall_entity_span
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value: 0.7995836706372152
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name: Token Classification
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type: token-classification
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dataset:
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name: tner/tweetner7/test_2020
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type: tner/tweetner7/test_2020
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args: tner/tweetner7/test_2020
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metrics:
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- name: F1
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type: f1
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value: 0.6439847577572129
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- name: Precision
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type: precision
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value: 0.6771608471665712
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- name: Recall
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type: recall
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value: 0.6139076284379865
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- name: F1 (
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type: f1_macro
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value: 0.6008744778169367
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- name: Precision (
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type: precision_macro
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value: 0.6358142893696356
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- name: Recall (
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type: recall_macro
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value: 0.5742193301311931
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- name: F1 (
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type: f1_entity_span
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value: 0.7552409474543968
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- name: Precision (
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type: precision_entity_span
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value: 0.7943871706758304
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- name: Recall (
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type: recall_entity_span
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value: 0.7197716658017644
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name: Token Classification
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type: token-classification
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dataset:
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name: tner/tweetner7
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type: tner/tweetner7
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args: tner/tweetner7
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metrics:
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- name: F1 (test_2021)
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type: f1
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value: 0.6632769652650823
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- name: Precision (test_2021)
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type: precision
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value: 0.6554878048780488
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+
- name: Recall (test_2021)
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type: recall
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value: 0.6712534690101758
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- name: Macro F1 (test_2021)
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type: f1_macro
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value: 0.6096477771855761
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- name: Macro Precision (test_2021)
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type: precision_macro
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value: 0.6042443991246051
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- name: Macro Recall (test_2021)
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type: recall_macro
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value: 0.6191008735553379
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- name: Entity Span F1 (test_2021)
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type: f1_entity_span
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value: 0.7900359938296291
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- name: Entity Span Precision (test_2020)
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type: precision_entity_span
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value: 0.780713640469738
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- name: Entity Span Recall (test_2021)
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type: recall_entity_span
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value: 0.7995836706372152
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- name: F1 (test_2020)
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type: f1
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value: 0.6439847577572129
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- name: Precision (test_2020)
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type: precision
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value: 0.6771608471665712
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- name: Recall (test_2020)
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type: recall
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value: 0.6139076284379865
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- name: Macro F1 (test_2020)
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type: f1_macro
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value: 0.6008744778169367
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- name: Macro Precision (test_2020)
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type: precision_macro
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value: 0.6358142893696356
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- name: Macro Recall (test_2020)
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type: recall_macro
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value: 0.5742193301311931
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- name: Entity Span F1 (test_2020)
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type: f1_entity_span
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value: 0.7552409474543968
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- name: Entity Span Precision (test_2020)
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type: precision_entity_span
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value: 0.7943871706758304
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- name: Entity Span Recall (test_2020)
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type: recall_entity_span
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value: 0.7197716658017644
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