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ModernBERT-large-nli

Zero-Shot Classification
Transformers
Safetensors
English
modernbert
text-classification
instruct
natural-language-inference
nli
Inference Endpoints
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Duplicate from tasksource/ModernBERT-large-nli

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Co-authored-by: Damien Sileo <[email protected]>

Files changed (7) hide show
  1. .gitattributes +35 -0
  2. README.md +193 -0
  3. config.json +329 -0
  4. model.safetensors +3 -0
  5. special_tokens_map.json +37 -0
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +945 -0
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README.md ADDED
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1
+ ---
2
+ library_name: transformers
3
+ base_model:
4
+ - answerdotai/ModernBERT-large
5
+ license: apache-2.0
6
+ language:
7
+ - en
8
+ pipeline_tag: zero-shot-classification
9
+ datasets:
10
+ - nyu-mll/glue
11
+ - facebook/anli
12
+ tags:
13
+ - instruct
14
+ - natural-language-inference
15
+ - nli
16
+ ---
17
+
18
+ # Model Card for Model ID
19
+
20
+ This model is ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table).
21
+ This is the equivalent of an "instruct" version.
22
+ The model was trained for 200k steps on an Nvidia A30 GPU.
23
+
24
+ It is very good at reasoning tasks (better than llama 3.1 8B Instruct on ANLI and FOLIO), long context reasoning, sentiment analysis and zero-shot classification with new labels.
25
+
26
+ The following table shows model test accuracy. These are the scores for the same single transformer with different classification heads on top.
27
+ Further gains can be obtained by fine-tuning on a single-task, e.g. SST, but it this checkpoint is great for zero-shot classification and natural language inference (contradiction/entailment/neutral classification).
28
+
29
+ | test_name | test_accuracy |
30
+ |:--------------------------------------|----------------:|
31
+ | glue/mnli | 0.89 |
32
+ | glue/qnli | 0.96 |
33
+ | glue/rte | 0.91 |
34
+ | glue/wnli | 0.64 |
35
+ | glue/mrpc | 0.81 |
36
+ | glue/qqp | 0.87 |
37
+ | glue/cola | 0.87 |
38
+ | glue/sst2 | 0.96 |
39
+ | super_glue/boolq | 0.66 |
40
+ | super_glue/cb | 0.86 |
41
+ | super_glue/multirc | 0.9 |
42
+ | super_glue/wic | 0.71 |
43
+ | super_glue/axg | 1 |
44
+ | anli/a1 | 0.72 |
45
+ | anli/a2 | 0.54 |
46
+ | anli/a3 | 0.55 |
47
+ | sick/label | 0.91 |
48
+ | sick/entailment_AB | 0.93 |
49
+ | snli | 0.94 |
50
+ | scitail/snli_format | 0.95 |
51
+ | hans | 1 |
52
+ | WANLI | 0.77 |
53
+ | recast/recast_ner | 0.85 |
54
+ | recast/recast_sentiment | 0.97 |
55
+ | recast/recast_verbnet | 0.89 |
56
+ | recast/recast_megaveridicality | 0.87 |
57
+ | recast/recast_verbcorner | 0.87 |
58
+ | recast/recast_kg_relations | 0.9 |
59
+ | recast/recast_factuality | 0.95 |
60
+ | recast/recast_puns | 0.98 |
61
+ | probability_words_nli/reasoning_1hop | 1 |
62
+ | probability_words_nli/usnli | 0.79 |
63
+ | probability_words_nli/reasoning_2hop | 0.98 |
64
+ | nan-nli | 0.85 |
65
+ | nli_fever | 0.78 |
66
+ | breaking_nli | 0.99 |
67
+ | conj_nli | 0.72 |
68
+ | fracas | 0.79 |
69
+ | dialogue_nli | 0.94 |
70
+ | mpe | 0.75 |
71
+ | dnc | 0.91 |
72
+ | recast_white/fnplus | 0.76 |
73
+ | recast_white/sprl | 0.9 |
74
+ | recast_white/dpr | 0.84 |
75
+ | add_one_rte | 0.94 |
76
+ | paws/labeled_final | 0.96 |
77
+ | pragmeval/pdtb | 0.56 |
78
+ | lex_glue/scotus | 0.58 |
79
+ | lex_glue/ledgar | 0.85 |
80
+ | dynasent/dynabench.dynasent.r1.all/r1 | 0.83 |
81
+ | dynasent/dynabench.dynasent.r2.all/r2 | 0.76 |
82
+ | cycic_classification | 0.96 |
83
+ | lingnli | 0.91 |
84
+ | monotonicity-entailment | 0.97 |
85
+ | scinli | 0.88 |
86
+ | naturallogic | 0.93 |
87
+ | dynahate | 0.86 |
88
+ | syntactic-augmentation-nli | 0.94 |
89
+ | autotnli | 0.92 |
90
+ | defeasible-nli/atomic | 0.83 |
91
+ | defeasible-nli/snli | 0.8 |
92
+ | help-nli | 0.96 |
93
+ | nli-veridicality-transitivity | 0.99 |
94
+ | lonli | 0.99 |
95
+ | dadc-limit-nli | 0.79 |
96
+ | folio | 0.71 |
97
+ | tomi-nli | 0.54 |
98
+ | puzzte | 0.59 |
99
+ | temporal-nli | 0.93 |
100
+ | counterfactually-augmented-snli | 0.81 |
101
+ | cnli | 0.9 |
102
+ | boolq-natural-perturbations | 0.72 |
103
+ | equate | 0.65 |
104
+ | logiqa-2.0-nli | 0.58 |
105
+ | mindgames | 0.96 |
106
+ | ConTRoL-nli | 0.66 |
107
+ | logical-fallacy | 0.38 |
108
+ | cladder | 0.89 |
109
+ | conceptrules_v2 | 1 |
110
+ | zero-shot-label-nli | 0.79 |
111
+ | scone | 1 |
112
+ | monli | 1 |
113
+ | SpaceNLI | 1 |
114
+ | propsegment/nli | 0.92 |
115
+ | FLD.v2/default | 0.91 |
116
+ | FLD.v2/star | 0.78 |
117
+ | SDOH-NLI | 0.99 |
118
+ | scifact_entailment | 0.87 |
119
+ | feasibilityQA | 0.79 |
120
+ | AdjectiveScaleProbe-nli | 1 |
121
+ | resnli | 1 |
122
+ | semantic_fragments_nli | 1 |
123
+ | dataset_train_nli | 0.95 |
124
+ | nlgraph | 0.97 |
125
+ | ruletaker | 0.99 |
126
+ | PARARULE-Plus | 1 |
127
+ | logical-entailment | 0.93 |
128
+ | nope | 0.56 |
129
+ | LogicNLI | 0.91 |
130
+ | contract-nli/contractnli_a/seg | 0.88 |
131
+ | contract-nli/contractnli_b/full | 0.84 |
132
+ | nli4ct_semeval2024 | 0.72 |
133
+ | biosift-nli | 0.92 |
134
+ | SIGA-nli | 0.57 |
135
+ | FOL-nli | 0.79 |
136
+ | doc-nli | 0.81 |
137
+ | mctest-nli | 0.92 |
138
+ | natural-language-satisfiability | 0.92 |
139
+ | idioms-nli | 0.83 |
140
+ | lifecycle-entailment | 0.79 |
141
+ | MSciNLI | 0.84 |
142
+ | hover-3way/nli | 0.92 |
143
+ | seahorse_summarization_evaluation | 0.81 |
144
+ | missing-item-prediction/contrastive | 0.88 |
145
+ | Pol_NLI | 0.93 |
146
+ | synthetic-retrieval-NLI/count | 0.72 |
147
+ | synthetic-retrieval-NLI/position | 0.9 |
148
+ | synthetic-retrieval-NLI/binary | 0.92 |
149
+ | babi_nli | 0.98 |
150
+
151
+
152
+
153
+ # Usage
154
+
155
+ ## [ZS] Zero-shot classification pipeline
156
+ ```python
157
+ from transformers import pipeline
158
+ classifier = pipeline("zero-shot-classification",model="tasksource/ModernBERT-large-nli")
159
+
160
+ text = "one day I will see the world"
161
+ candidate_labels = ['travel', 'cooking', 'dancing']
162
+ classifier(text, candidate_labels)
163
+ ```
164
+ NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
165
+
166
+ ## [NLI] Natural language inference pipeline
167
+
168
+ ```python
169
+ from transformers import pipeline
170
+ pipe = pipeline("text-classification",model="tasksource/ModernBERT-large-nli")
171
+ pipe([dict(text='there is a cat',
172
+ text_pair='there is a black cat')]) #list of (premise,hypothesis)
173
+ ```
174
+
175
+ ## Backbone for further fune-tuning
176
+
177
+ This checkpoint has stronger reasoning and fine-grained abilities than the base version and can be used for further fine-tuning.
178
+
179
+ # Citation
180
+
181
+ ```
182
+ @inproceedings{sileo-2024-tasksource,
183
+ title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
184
+ author = "Sileo, Damien",
185
+ booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
186
+ month = may,
187
+ year = "2024",
188
+ address = "Torino, Italia",
189
+ publisher = "ELRA and ICCL",
190
+ url = "https://aclanthology.org/2024.lrec-main.1361",
191
+ pages = "15655--15684",
192
+ }
193
+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "answerdotai/ModernBERT-large",
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+ "architectures": [
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+ "ModernBertForSequenceClassification"
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+ ],
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+ "0": "entailment",
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+ "1": "neutral",
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+ "2": "contradiction"
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+ },
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+ "initializer_cutoff_factor": 2.0,
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+ "intermediate_size": 2624,
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+ "sep_token_id": 50282,
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+ "sparse_pred_ignore_index": -100,
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+ "sparse_prediction": false,
190
+ "tasks": [
191
+ "glue/mnli",
192
+ "glue/qnli",
193
+ "glue/rte",
194
+ "glue/wnli",
195
+ "glue/mrpc",
196
+ "glue/qqp",
197
+ "glue/stsb",
198
+ "super_glue/boolq",
199
+ "super_glue/cb",
200
+ "super_glue/multirc",
201
+ "super_glue/wic",
202
+ "super_glue/axg",
203
+ "anli/a1",
204
+ "anli/a2",
205
+ "anli/a3",
206
+ "sick/label",
207
+ "sick/entailment_AB",
208
+ "snli",
209
+ "scitail/snli_format",
210
+ "hans",
211
+ "WANLI",
212
+ "recast/recast_ner",
213
+ "recast/recast_sentiment",
214
+ "recast/recast_verbnet",
215
+ "recast/recast_megaveridicality",
216
+ "recast/recast_verbcorner",
217
+ "recast/recast_kg_relations",
218
+ "recast/recast_factuality",
219
+ "recast/recast_puns",
220
+ "probability_words_nli/reasoning_1hop",
221
+ "probability_words_nli/usnli",
222
+ "probability_words_nli/reasoning_2hop",
223
+ "nan-nli",
224
+ "nli_fever",
225
+ "breaking_nli",
226
+ "conj_nli",
227
+ "fracas",
228
+ "dialogue_nli",
229
+ "mpe",
230
+ "dnc",
231
+ "recast_white/fnplus",
232
+ "recast_white/sprl",
233
+ "recast_white/dpr",
234
+ "robust_nli/IS_CS",
235
+ "robust_nli/LI_LI",
236
+ "robust_nli/ST_WO",
237
+ "robust_nli/PI_SP",
238
+ "robust_nli/PI_CD",
239
+ "robust_nli/ST_SE",
240
+ "robust_nli/ST_NE",
241
+ "robust_nli/ST_LM",
242
+ "robust_nli_is_sd",
243
+ "robust_nli_li_ts",
244
+ "add_one_rte",
245
+ "paws/labeled_final",
246
+ "glue/cola",
247
+ "glue/sst2",
248
+ "pragmeval/pdtb",
249
+ "lex_glue/eurlex",
250
+ "lex_glue/scotus",
251
+ "lex_glue/ledgar",
252
+ "lex_glue/unfair_tos",
253
+ "dynasent/dynabench.dynasent.r1.all/r1",
254
+ "dynasent/dynabench.dynasent.r2.all/r2",
255
+ "cycic_classification",
256
+ "lingnli",
257
+ "monotonicity-entailment",
258
+ "scinli",
259
+ "naturallogic",
260
+ "dynahate",
261
+ "syntactic-augmentation-nli",
262
+ "autotnli",
263
+ "defeasible-nli/atomic",
264
+ "defeasible-nli/snli",
265
+ "help-nli",
266
+ "nli-veridicality-transitivity",
267
+ "lonli",
268
+ "dadc-limit-nli",
269
+ "folio",
270
+ "tomi-nli",
271
+ "puzzte",
272
+ "temporal-nli",
273
+ "counterfactually-augmented-snli",
274
+ "cnli",
275
+ "boolq-natural-perturbations",
276
+ "equate",
277
+ "chaos-mnli-ambiguity",
278
+ "logiqa-2.0-nli",
279
+ "mindgames",
280
+ "ConTRoL-nli",
281
+ "logical-fallacy",
282
+ "cladder",
283
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