Upload model
Browse files- README.md +37 -0
- added_tokens.json +4 -0
- config.json +419 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +14 -0
- vocab.txt +0 -0
README.md
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---
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license: apache-2.0
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library_name: span-marker
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tags:
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- span-marker
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- token-classification
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- ner
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- named-entity-recognition
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pipeline_tag: token-classification
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---
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# SpanMarker for Named Entity Recognition
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder.
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## Usage
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To use this model for inference, first install the `span_marker` library:
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```bash
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pip install span_marker
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```
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You can then run inference with this model like so:
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```python
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from span_marker import SpanMarkerModel
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# Download from the 🤗 Hub
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model = SpanMarkerModel.from_pretrained("span_marker_model_name")
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# Run inference
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entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
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```
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See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
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added_tokens.json
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{
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"<end>": 28997,
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"<start>": 28996
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}
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config.json
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{
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"_name_or_path": "models\\span_marker_bert_base_cross_ner_P3ps\\checkpoint-final",
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"architectures": [
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"SpanMarkerModel"
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],
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"encoder": {
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"_name_or_path": "bert-base-cased",
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"add_cross_attention": false,
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"bad_words_ids": null,
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"begin_suppress_tokens": null,
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"bos_token_id": null,
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"chunk_size_feed_forward": 0,
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"classifier_dropout": null,
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"cross_attention_hidden_size": null,
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"decoder_start_token_id": null,
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"diversity_penalty": 0.0,
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"do_sample": false,
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"early_stopping": false,
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"encoder_no_repeat_ngram_size": 0,
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"eos_token_id": null,
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"exponential_decay_length_penalty": null,
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"finetuning_task": null,
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"forced_bos_token_id": null,
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"forced_eos_token_id": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "O",
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"1": "B-academicjournal",
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"2": "I-academicjournal",
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"3": "B-album",
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"4": "I-album",
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"5": "B-algorithm",
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"6": "I-algorithm",
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"7": "B-astronomicalobject",
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"8": "I-astronomicalobject",
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"9": "B-award",
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"10": "I-award",
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"11": "B-band",
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"12": "I-band",
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"13": "B-book",
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"14": "I-book",
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"15": "B-chemicalcompound",
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"16": "I-chemicalcompound",
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"17": "B-chemicalelement",
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"18": "I-chemicalelement",
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"19": "B-conference",
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"20": "I-conference",
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"21": "B-country",
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"22": "I-country",
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"23": "B-discipline",
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"24": "I-discipline",
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"25": "B-election",
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"26": "I-election",
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"27": "B-enzyme",
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"28": "I-enzyme",
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"29": "B-event",
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"30": "I-event",
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"31": "B-field",
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"32": "I-field",
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"33": "B-literarygenre",
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"34": "I-literarygenre",
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"35": "B-location",
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"36": "I-location",
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"37": "B-magazine",
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"38": "I-magazine",
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"39": "B-metrics",
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"40": "I-metrics",
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"41": "B-misc",
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"42": "I-misc",
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"43": "B-musicalartist",
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"44": "I-musicalartist",
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"45": "B-musicalinstrument",
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"46": "I-musicalinstrument",
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"47": "B-musicgenre",
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"48": "I-musicgenre",
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"49": "B-organisation",
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"50": "I-organisation",
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"51": "B-person",
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"52": "I-person",
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"53": "B-poem",
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"54": "I-poem",
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"55": "B-politicalparty",
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"56": "I-politicalparty",
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"57": "B-politician",
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"58": "I-politician",
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"59": "B-product",
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"60": "I-product",
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"61": "B-programlang",
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"62": "I-programlang",
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"63": "B-protein",
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"64": "I-protein",
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"65": "B-researcher",
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"66": "I-researcher",
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"67": "B-scientist",
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"68": "I-scientist",
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"69": "B-song",
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"70": "I-song",
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"71": "B-task",
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"72": "I-task",
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"73": "B-theory",
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"74": "I-theory",
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"75": "B-university",
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"76": "I-university",
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"77": "B-writer",
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"78": "I-writer"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"is_decoder": false,
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"is_encoder_decoder": false,
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"label2id": {
|
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"B-academicjournal": 1,
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"B-album": 3,
|
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"B-algorithm": 5,
|
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"B-astronomicalobject": 7,
|
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"B-award": 9,
|
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"B-band": 11,
|
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"B-book": 13,
|
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"B-chemicalcompound": 15,
|
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"B-chemicalelement": 17,
|
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"B-conference": 19,
|
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"B-country": 21,
|
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"B-discipline": 23,
|
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"B-election": 25,
|
132 |
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"B-enzyme": 27,
|
133 |
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"B-event": 29,
|
134 |
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"B-field": 31,
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135 |
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"B-literarygenre": 33,
|
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"B-location": 35,
|
137 |
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"B-magazine": 37,
|
138 |
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"B-metrics": 39,
|
139 |
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"B-misc": 41,
|
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"B-musicalartist": 43,
|
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"B-musicalinstrument": 45,
|
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"B-musicgenre": 47,
|
143 |
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"B-organisation": 49,
|
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"B-person": 51,
|
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"B-poem": 53,
|
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+
"B-politicalparty": 55,
|
147 |
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"B-politician": 57,
|
148 |
+
"B-product": 59,
|
149 |
+
"B-programlang": 61,
|
150 |
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"B-protein": 63,
|
151 |
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"B-researcher": 65,
|
152 |
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"B-scientist": 67,
|
153 |
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"B-song": 69,
|
154 |
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"B-task": 71,
|
155 |
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"B-theory": 73,
|
156 |
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"B-university": 75,
|
157 |
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"B-writer": 77,
|
158 |
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"I-academicjournal": 2,
|
159 |
+
"I-album": 4,
|
160 |
+
"I-algorithm": 6,
|
161 |
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"I-astronomicalobject": 8,
|
162 |
+
"I-award": 10,
|
163 |
+
"I-band": 12,
|
164 |
+
"I-book": 14,
|
165 |
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"I-chemicalcompound": 16,
|
166 |
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"I-chemicalelement": 18,
|
167 |
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"I-conference": 20,
|
168 |
+
"I-country": 22,
|
169 |
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"I-discipline": 24,
|
170 |
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"I-election": 26,
|
171 |
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"I-enzyme": 28,
|
172 |
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"I-event": 30,
|
173 |
+
"I-field": 32,
|
174 |
+
"I-literarygenre": 34,
|
175 |
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"I-location": 36,
|
176 |
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"I-magazine": 38,
|
177 |
+
"I-metrics": 40,
|
178 |
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"I-misc": 42,
|
179 |
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"I-musicalartist": 44,
|
180 |
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"I-musicalinstrument": 46,
|
181 |
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"I-musicgenre": 48,
|
182 |
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"I-organisation": 50,
|
183 |
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"I-person": 52,
|
184 |
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"I-poem": 54,
|
185 |
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"I-politicalparty": 56,
|
186 |
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"I-politician": 58,
|
187 |
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"I-product": 60,
|
188 |
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"I-programlang": 62,
|
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"I-protein": 64,
|
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"I-researcher": 66,
|
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"I-scientist": 68,
|
192 |
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"I-song": 70,
|
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"I-task": 72,
|
194 |
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"I-theory": 74,
|
195 |
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"I-university": 76,
|
196 |
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"I-writer": 78,
|
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"O": 0
|
198 |
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},
|
199 |
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"layer_norm_eps": 1e-12,
|
200 |
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"length_penalty": 1.0,
|
201 |
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"max_length": 20,
|
202 |
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"max_position_embeddings": 512,
|
203 |
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"min_length": 0,
|
204 |
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"model_type": "bert",
|
205 |
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"no_repeat_ngram_size": 0,
|
206 |
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"num_attention_heads": 12,
|
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"num_beam_groups": 1,
|
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"num_beams": 1,
|
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"num_hidden_layers": 12,
|
210 |
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"num_return_sequences": 1,
|
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"output_attentions": false,
|
212 |
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"output_hidden_states": false,
|
213 |
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"output_scores": false,
|
214 |
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"pad_token_id": 0,
|
215 |
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"position_embedding_type": "absolute",
|
216 |
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"prefix": null,
|
217 |
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"problem_type": null,
|
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"pruned_heads": {},
|
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
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vocab.txt
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