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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,36 @@
+
+---
+license: apache-2.0
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+pipeline_tag: token-classification
+---
+
+# SpanMarker for Named Entity Recognition
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder. 
+
+## Usage
+
+To use this model for inference, first install the `span_marker` library:
+
+```bash
+pip install span_marker
+```
+
+You can then run inference with this model like so:
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_name")
+# Run inference
+entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
+```
+
+See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.

added_tokens.json

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+  "<start>": 250002
+}

config.json

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+{
+  "_name_or_path": "models/span_marker_xlm_roberta_base_multinerd/checkpoint-final",
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "xlm-roberta-base",
+    "add_cross_attention": false,
+    "architectures": [
+      "XLMRobertaForMaskedLM"
+    ],
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+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "O",
+      "1": "B-PER",
+      "2": "I-PER",
+      "3": "B-ORG",
+      "4": "I-ORG",
+      "5": "B-LOC",
+      "6": "I-LOC",
+      "7": "B-ANIM",
+      "8": "I-ANIM",
+      "9": "B-BIO",
+      "10": "I-BIO",
+      "11": "B-CEL",
+      "12": "I-CEL",
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+      "14": "I-DIS",
+      "15": "B-EVE",
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+      "17": "B-FOOD",
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+      "20": "I-INST",
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+      "24": "I-MYTH",
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+      "28": "I-TIME",
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+      "30": "I-VEHI"
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+  "marker_max_length": 128,
+  "max_next_context": null,
+  "max_prev_context": null,
+  "model_max_length": 256,
+  "model_max_length_default": 512,
+  "model_type": "span-marker",
+  "span_marker_version": "1.2.5.dev",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.28.1",
+  "vocab_size": 250004
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer.json

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tokenizer_config.json

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+}